Notably , each of these systems directly predicts graphemes in the written domain , without using an external pronunciation lexicon , or a separate language model . We examine several sequence-to-sequence models including connectionist temporal classification ( CTC ) , the recurrent neural network ( RNN ) transducer , an attentionbased model , and a model which augments the RNN transducer with an attention mechanism . We find that the sequence-to-sequence models are competitive with traditional state-of-the-art approaches on dictation test sets , although the baseline , which uses a separate pronunciation and language model , outperforms these models on voice-search test sets . Probabilistic models are a critical part of the modern deep learning toolbox - ranging from generative models ( VAEs , GANs ) , sequence to sequence models used in machine translation and speech processing to models over functional spaces ( conditional neural processes , neural processes ) . Given the size and complexity of these models , safely deploying them in applications requires the development of tools to analyze their behavior rigorously and provide some guarantees that these models are consistent with a list of desirable properties or specifications . Our model first generates a set of talking point phrases as intermediate representation , followed by a separate decoder producing the final argument based on both input and the keyphrases . Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than a popular sequence-to-sequence generation model according to both automatic evaluation and human assessments . In this article , we investigate whispered-to natural-speech conversion method using sequence to sequence generation approach by proposing modified transformer architecture . We investigate different kinds of features such as mel frequency cepstral coefficients ( MFCCs ) and smoothed spectral features . MOTIVATION In recent years , several methods have been proposed for aligning two protein sequence profiles , with reported improvements in alignment accuracy and homolog discrimination versus sequence-sequence methods ( e.g. BLAST ) and profile-sequence methods ( e.g. PSI-BLAST ) . Profile-profile alignment is also the iterated step in progressive multiple sequence alignment algorithms such as CLUSTALW . To our knowledge , this is the largest publicly available human labeled data set for open information extraction tasks . Using this labeled SAOKE data set , we train an end-to-end neural model using the sequence-to-sequence paradigm , called Logician , to transform sentences into facts . For each sentence , different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts , Logician performs a global optimization over all possible involved facts , in which facts not only compete with each other to attract the attention of words , but also cooperate to share words . Synthesizing SQL queries from natural language is a long-standing open problem and has been attracting considerable interest recently . Toward solving the problem , the de facto approach is to employ a sequence-to-sequence-style model . Such an approach will necessarily require the SQL queries to be serialized . Since the same SQL query may have multiple equivalent serializations , training a sequence-to-sequence-style model is sensitive to the choice from one of them . This phenomenon is documented as the " order-matters " problem . We explore a novel approach for Semantic Role Labeling ( SRL ) by casting it as a sequence-to-sequence process . We employ an attention-based model enriched with a copying mechanism to ensure faithful regeneration of the input sequence , while enabling interleaved generation of argument role labels . Since our self-training does not change the model structure , it does not influence the efficiency of translation , such as the translation speed . The experimental results showed that the translation quality improved not only in the sequence-to-sequence ( seq-to-seq ) models but also in the transformer models . This allows every position in the decoder to attend over all positions in the input sequence . This mimics the typical encoder - decoder attention mechanisms in sequence - to - sequence models such as wu2016google , bahdanau2014neural , JonasFaceNet2017 . The encoder contains self - attention layers . This task can also be naturally cast as mapping an input sequence of words in a source document to a target sequence of words called summary . In the recent past , deep - learning based models that map an input sequence into another output sequence , called sequence - to - sequence models , have been successful in many problems such as machine translation , speech recognition and video captioning . In the framework of sequence - to - sequence models , a very relevant model to our task is the attentional Recurrent Neural Network ( RNN ) encoder - decoder model proposed in nmt , which has produced state - of - the - art performance in machine translation ( MT ) , which is also a natural language task . The factorization turns the joint modeling problem into a sequence problem , where one learns to predict the next pixel given all the previously generated pixels . But to model the highly nonlinear and long - range correlations between pixels and the complex conditional distributions that result , a highly expressive sequence model is necessary . Recurrent Neural Networks ( RNN ) are powerful models that offer a compact , shared parametrization of a series of conditional distributions . We find that the bare SPINN - PI - NT model performs little better than the RNN baseline , but that SPINN - PI with the added tracking LSTM performs well . The success of SPINN - PI , which is the hybrid tree - sequence model , suggests that the tree - and sequence - based encoding methods are at least partially complementary , with the sequence model presumably providing useful local word disambiguation . The full SPINN model with its relatively weak internal parser performs slightly less well , but nonetheless robustly exceeds the performance of the RNN baseline . Our starting point is previous work on Graph Neural Networks scarselli2009graph , which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences . The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence - based models ( e.g. , LSTMs ) when the problem is graph - structured . We demonstrate the capabilities on some simple AI ( bAbI ) and graph algorithm learning tasks . However , such methods may not perform well due to the loss of word order information . More recent models for distributed sentence representation fall into two categories according to the form of input sentence : sequence - based models and tree - structured models . Sequence - based models construct sentence representations from word sequences by taking in account the relationship between successive words . The inputs to WaveNet ( linguistic features , predicted log fundamental frequency ( ) , and phoneme durations ) , however , require significant domain expertise to produce , involving elaborate text - analysis systems as well as a robust lexicon ( pronunciation guide ) . Tacotron , a sequence - to - sequence architecture for producing magnitude spectrograms from a sequence of characters , simplifies the traditional speech synthesis pipeline by replacing the production of these linguistic and acoustic features with a single neural network trained from data alone . To vocode the resulting magnitude spectrograms , Tacotron uses the Griffin - Lim algorithm for phase estimation , followed by an inverse short - time Fourier transform . section : Conclusion This paper describes Tacotron 2 , a fully neural TTS system that combines a sequence - to - sequence recurrent network with attention to predicts mel spectrograms with a modified WaveNet vocoder . The resulting system synthesizes speech with Tacotron - level prosody and WaveNet - level audio quality . Machine translation using deep neural networks achieved great success with sequence - to - sequence models that used recurrent neural networks ( RNNs ) with long short - term memory ( LSTM , ) cells . The basic sequence - to - sequence architecture is composed of an RNN encoder which reads the source sentence one token at a time and transforms it into a fixed - sized state vector . This is followed by an RNN decoder , which generates the target sentence , one token at a time , from the state vector . In this paper , we propose a new method for parallel corpus mining based on multilingual sentence embeddings . Our approach uses a sequence - to - sequence architecture to train a multilingual sentence encoder on an initial parallel corpus , and a novel margin - based scoring method that overcomes the scale inconsistencies of cosine similarity . Our experiments show large improvements over previous methods in all the tested scenarios . document : Graph2Seq : Graph to Sequence Learning with Attention - Based Neural Networks The celebrated Sequence to Sequence learning ( Seq2Seq ) technique and its numerous variants achieve excellent performance on many tasks . However , many machine learning tasks have inputs naturally represented as graphs ; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence . In this section , we describe these representations of the SQL query . subsection : Sequence Representation We apply a simple template to construct the SQL query sequence : “ SELECT + aggregation function > + Split Symbol + selected column > Unlike S - LSTM , ANF composes the child nodes attentively in respect to another relevant input vector . The input vector can be a learnable representation from a sequence representation . Given a matrix resulted by concatenating the child node representations , and the third input vector , ANF is defined as where is a learnable matrix , the attention score and the attention weight vector for each child . section : Introduction Sequence to sequence learning has been successful in many tasks such as machine translation , speech recognition [ reference ] and text summarization [ reference ][ reference ][ reference ] amongst others . The dominant approach to date encodes the input sequence with a series of bi - directional recurrent neural networks ( RNN ) and generates a variable length output with another set of decoder RNNs , both of which interface via a soft - attention mechanism [ reference ] . Therefore , in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses . We designed a sequence - to - sequence network composed of layer - normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training . We found that the knowledge of temporal consistency improves the best reported result on Human3.6 M dataset by approximately and helps our network to recover temporally consistent 3D poses over a sequence of images even when the 2D pose detector fails . Our LSTM model significantly differs from all these models , as it makes no assumptions about the task . As a sequence - to - sequence prediction model it is somewhat related to the incremental parsing models , pioneered by and extended by . Such linear time parsers however typically need some task - specific constraints and might build up the parse in multiple passes . In particular , there is no need for language features like part - of - speech tags or dependency parses . Furthermore , a generic sequence - to - sequence solution allows to extend the concept of capturing entailment across any sequential data , not only natural language . Recently , [ reference ] published Second , an attentive hierarchical recurrent encoder ( AHRE ) is designed which is capable to encode sentences hierarchically and generate more descriptive representations by aggregation . Third , a new pooling method which combines multi-dimensional pooling and last-state pooling is used instead of the simple combination of max pooling and average pooling in the original ESIM . Last , a modification layer is added before the softmax layer to emphasize the importance of the last utterance in the context for response selection . Decision-making algorithms are proposed for buyers , contractors , and subcontractors , each of which has a different optimization goal . We have built a wireless network simulator , called ESIM , to assist in the design and analysis of these algorithms . When the average communication energy required transferring a task is less than the average energy required to execute a task , our experimental results indicate that markets based on our protocol and decision-making algorithms fairly and effectively allocate energy resources among different tasks in both cooperative and competitive scenarios . First , we train the word2vec model to transform the address records into their corresponding vector representations . Next , we apply the enhanced sequential inference model ( ESIM ) , a deep text-matching model , to make local and global inferences to determine if two addresses match . To evaluate the accuracy of the proposed method , we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods . Moreover , current studies reveal contradictory findings and provide a fragmented understanding . The purpose of this paper is to set before the reader an ESIM adaptation framework underpinned by a morphogenetic approach . Our qualitative study resulted in a richer description of ESIM and its adaptation . Moreover , current studies reveal contradictory findings and provide a fragmented understanding . The purpose of this paper is to set before the reader an ESIM adaptation framework underpinned by a morphogenetic approach . Our qualitative study resulted in a richer description of ESIM and its adaptation . The ECM Signal Monitor ( ESIM ) , which was designed and constructed to monitor the IF and baseband outputs of wideband digital radios , is described . The ESIM system utilizes the wideband radio outputs to determine if the monitored radio is being jammed , and characterizes the type and intensity of the jamming , if present . The hardware and software elements of the ESIM are described , as are the ESIM jammer detection and characterization algorithms . The embodiment of the invention discloses an ESIM number writing method which is used for improving the safety of writing an ESIM number and avoiding risks that the ESIM number is maliciously hijacked or tampered and the like . The embodiment method of the invention comprises the steps : a security system receives an ESIM number writing instruction ; if the safety system queries that the ESIM number writing instruction matches with a ESIM number server white list , the system sends an ESIM number writing request to a corresponding ESIM number server through a virtual private network ( VPN ) according to the ESIM number writing instruction ; the security system receives encrypted ESIM number data sent by the ESIM number server based on the ESIM number writing request ; the security decodes the encrypted ESIM number data ; and the security system writes the decrypted ESIM number data in ESIM equipment . CED features a wide variety of indoor and outdoor scenes , which we hope will help drive forward event-based vision research . We also present an extension of the event camera simulator ESIM that enables simulation of color events . Finally , we present an evaluation of three state-of-the-art image reconstruction methods that can be used to convert the Color-DAVIS346 into a continuous-time , HDR , color video camera to visualise the event stream , and for use in downstream vision applications . These dialog systems must learn to interact with external domain knowledge to achieve the desired goal e.g. recommending courses to a student , booking a table at a restaurant etc . This paper presents extended Enhanced Sequential Inference Model ( ESIM ) models : a ) K-ESIM ( Knowledge-ESIM ) , which incorporates the external domain knowledge and b ) T-ESIM ( Targeted-ESIM ) , which leverages information from similar conversations to improve the prediction accuracy . Our proposed models and the baseline ESIM model are evaluated on the Ubuntu and Advising datasets in the Sentence Selection track of the latest Dialog System Technology Challenge ( DSTC7 ) , where the goal is to find the correct next utterance , given a partial conversation , from a set of candidates . For the document retrieval , we implemented a new entity linking approach . In order to be able to rank candidate facts and classify a claim on the basis of several selected facts , we introduce two extensions to the Enhanced LSTM ( ESIM ) . To protect services that are already operating or plan to operate in the Ka ( 27.5 - 29.5 GHz ) band , it is very essential to assess the interference from earth stations in motion ( ESIMs ) if ESIMs are to be deployed in this band . This paper proposes a methodology and a scheme that can respectively be employed to evaluate and mitigate the interference from Maritime ESIM ( M-ESIM ) to three incumbent services ; point-to-point fixed service ( PTP-FS ) , point-to-multipoint fixed service ( PMP-FS ) and mobile service ( MS ) of the Ka band . Simulation is carried out taking into consideration M-ESIM ’s off-axis angles and the distance between M-ESIM and the incumbent services . Based on the statistical analysis of flowrate in stream , a new type of index called ‘ Probabilistic Scheduling Index ’ was proposed in this work . The expert decision on a particular type of scheduling ( Fixed/Arranged/Demand ) was based on this index together with other site specific conditon The expert system ESIM was applied to an irrigation management problem of the Mae-Taeng Irrigation Project located in northern Thailand . Output of the expert system was found to be reasonable and demonstrated results could be used to assist in improving water management decisions . Such models are general reading architectures . To demonstrate that our reading module can be integrated into arbitrary task architectures , we also add our refinement module to a reimplementation of a state of the art architecture for RTE called ESIM Chen2017_ESIM . We refer the interested reader to the ESIM paper for details of the model . suppose , recognize , forget , remember , imagine , mean , agree , disagree , deny , promise . Table [ reference ] shows the frequency of aforementioned annotation tags in the SNLI test set along with the performance ( accuracy ) of ESIM , DR - BiLSTM ( Single ) , and DR - BiLSTM ( Ensemble ) . Table [ reference ] can be divided into four major categories : 1 ) gold label data , 2 ) word overlap , 3 ) sentence length , and 4 ) occurrence of special words . • E1 . ESIM [ reference ] . We train the base ESIM model without the tree - LSTM , as in [ reference ] , on the task of natural language inference with ADAM for 10 epochs on MultiNLI and also SNLI . Training Schedules : We use the following batch size schedules DBLP : conf / acl / ChenZLWJI17 proposed an enhanced sequential inference model ( ESIM ) , which is one of the best models so far and is used as one of our baselines in this paper . In this paper we enrich neural - network - based NLI models with external knowledge . Our inference model converts the output hidden vectors of BiLSTMs to the fixed - length vector with pooling operations and puts it into the final classifier to determine the overall inference class . Particularly , in addition to using mean pooling and max pooling similarly to ESIM DBLP : conf / acl / ChenZLWJI17 , we propose to use weighted pooling based on external knowledge to obtain a fixed - length vector as in Equation ( [ reference ] ) ( [ reference ] ) . In our experiments , we regard the function as a 1 - layer feed - forward neural network with ReLU activation function . Note that the above hyperparameter settings are same as those used in the baseline ESIM DBLP : conf / acl / ChenZLWJI17 model . ESIM is a strong NLI baseline framework with the source code made available at https: // github.com / lukecq1231 / nli ( the ESIM core code has also been adapted to summarization DBLP : conf / ijcai / ChenZLWJ16 and question - answering tasks Zhang : qa:2017 ) . The trade - off for calculating co - attention in Equation ( [ reference ] ) is selected in based on the development set . The Stanford Natural Language Inference ( SNLI ) corpus snliemnlp2015 provides approximately 550 K hypothesis / premise pairs . Our baseline , the ESIM sequence model from Chen2017EnhancedLF , uses a biLSTM to encode the premise and hypothesis , followed by a matrix attention layer , a local inference layer , another biLSTM inference composition layer , and finally a pooling operation before the output layer . Overall , adding ELMo to the ESIM model improves accuracy by an average of 0.7 % across five random seeds . We present Deep Voice , a production-quality text-to-speech system constructed entirely from deep neural networks . Deep Voice lays the groundwork for truly end-to-end neural speech synthesis . The system comprises five major building blocks : a segmentation model for locating phoneme boundaries , a grapheme-to-phoneme conversion model , a phoneme duration prediction model , a fundamental frequency prediction model , and an audio synthesis model . This situation is changing with the deployment of deep neural networks into the synthesis process . Projects including WaveNet , Deep Voice , Voice Loop , and many others generate very natural and high-quality speech that may clone voice identity . We are slowly approaching an era where we will not be able to recognize a genuine voice from a synthesized one . We present Deep Voice 3 , a fully-convolutional attention-based neural text-to-speech ( TTS ) system . Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster . Scaling Text-to-Speech with Convolutional Sequence Learning . We present Deep Voice 3 , a fully-convolutional attention-based neural text-to-speech ( TTS ) system . Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster . This paper examines some of the emerging technologies and speculates about the potentialities applications and digital convergence hold for online news edia . It examines five specific technologies – audio and video streaming , Third Voice , ultimedia digital editing software , Wireless Application Protocol with Cyberdisplay and mmersive digital videography . Exciting innovations , technologies and protocols under evelopment such as the prototypical virtual news reader , Ananova , V2ML , Bluetooth , ntelligent Equipment and Virtual Vision offer the promise of a wireless , streamlined lectronic news production process . The worst-case alignment time is then computed for a gate using a precharacterization approach , requiring only eight sample points while maintaining a small error . The proposed methods were implemented in an industrial noise analysis tool called ClariNet . Results on industrial designs , including a large PPCmicroprocessor design , are presented to demonstrate the effectiveness of our approach . Most modern text-to-speech architectures use a WaveNet vocoder for synthesizing high-fidelity waveform audio , but there have been limitations , such as high inference time , in its practical application due to its ancestral sampling scheme . The recently suggested Parallel WaveNet and ClariNet have achieved real-time audio synthesis capability by incorporating inverse autoregressive flow for parallel sampling . However , these approaches require a two-stage training pipeline with a well-trained teacher network and can only produce natural sound by using probability distillation along with auxiliary loss terms . To model the unique characteristic of different voices , low dimensional trainable speaker embeddings are shared across each component of ClariNet and trained together with the rest of the model . We demonstrate that the multi-speaker ClariNet outperforms state-of-the-art systems in terms of naturalness , because the whole model is jointly optimized in an end-to-end manner . When compared with the performance of C-MEX files generated by the MathWorks MATLAB compiler , we observed that , for our tests , the Fortran 90 programs ran faster than the corresponding C-MEX programs on an SGI Power Challenge and on a Sun SPARCstation 10 . This better performance is mainly attributed to our enhanced inference mechanism . When compared with the performance of C-MEX files generated by the MathWorks MATLAB compiler , we observed that , for our tests , the Fortran 90 programs ran faster than the corresponding C-MEX programs on an SGI Power Challenge and on a Sun SPARCstation 10 . This better performance is mainly attributed to our enhanced inference mechanism . ftn$\sp1$MATLAB is a trademark of The MathWorks , Inc. Based on the enhanced inference operator and the DR matrix , a new hybrid diagnostic method is established . Finally , a signal modulating circuit is used to verify the effectiveness of the enhanced inference operator on the D matrix , the R matrix and the DR matrix , which demonstrates the high efficiency of the enhanced inference operator and the feasibility of the new hybrid diagnostic method . Although most of these models have claimed state-of-the-art performance , the original papers often reported on only one or two selected datasets . We provide a systematic study and show that ( i ) encoding contextual information by LSTM and inter-sentence interactions are critical , ( ii ) Tree-LSTM does not help as much as previously claimed but surprisingly improves performance on Twitter datasets , ( iii ) the Enhanced Sequential Inference Model is the best so far for larger datasets , while the Pairwise Word Interaction Model achieves the best performance when less data is available . We release our implementations as an open-source toolkit . Our model consists of a universal layer shared for all tasks with several task-specific layers on top for each task . To generate universal representation , we employ the enhanced sequential inference model based on a deep learning and soft alignment techniques . The task-specific layers are composed of multilayer perceptrons . And the label inference is based on the smoothness assumption of semi-supervised learning . In this study , we propose an enhanced label inference approach which incorporates the importance of each vertex into the existing inference algorithms to improve the prediction capabilities of the algorithms . We also present extensions of three algorithms which are capable of taking the vertex importance variable to apply in learning . For that , model inference methods adapted to security testing can be used . This document tries to give some directions in order to combine enhanced model inference and model testing to ensure security of services automatically . The extreme e ciency of sequential search and the natural tendency of tree prun ing systems to produce wide variations in workload partly explains why it is proving di cult to achieve more than e ciency for massively parallel implementations of the algorithm Here we introduce typical enhanced sequential algorithms and address the major issues of parallel game tree searching under conditions of severe pruning It is this pruning that makes the parallelization di cult After examining previous work on parallel algorithms we present a new method called Dynamic Multiple Principal Variation Splitting DM PVSplit and implement it on the AP In this algorithm high performance is achieved by using some novel approaches Paral lel speculative search of candidate principal variations is used to reduce re search delay and so obtain more quickly a better estimate of the subtree value This is achieved by con guring a at processor arrangement as a dynamically changeable tree structure Also with the aid of a group based scheduling strategy the game tree is split dynam ically at di erent levels This provides better load balance and takes more advantage of parallelism Preliminary experiments show that the scalability of the DM PVSplit algorithm is good for massively parallel machines In this paper , we present a new state-of-the-art result , achieving the accuracy of 88.3 % on the standard benchmark , the Stanford Natural Language Inference dataset . This result is achieved first through our enhanced sequential encoding model , which outperforms the previous best model that employs more complicated network architectures , suggesting that the potential of sequential LSTM-based models have not been fully explored yet in previous work . We further show that by explicitly considering recursive architectures , we achieve additional improvement . Compared with open domain data , clinical texts present unique linguistic phenomena , e.g. , a large number of medical terms and abbreviations , different written forms for the same medical concept , which make inference much harder . Incorporating domain-specific knowledge is a way to eliminate this problem , in this paper , we assemble a new incorporating medical concept definitions module on the classic enhanced sequential inference model ( ESIM ) , which first extracts the most relevant medical concept for each word , if it exists , then encodes the definition of this medical concept with a bidirectional long short-term network ( BiLSTM ) to obtain domain-specific definition representations , and attends these definition representations over vanilla word embeddings . The empirical evaluations are conducted to demonstrate that our model improves the prediction performance and achieves a high level of accuracy on the MedNLI dataset . DBLP : conf / acl / ChenZLWJI17 proposed an enhanced sequential inference model ( ESIM ) , which is one of the best models so far and is used as one of our baselines in this paper . In this paper we enrich neural - network - based NLI models with external knowledge .
Notably , each of these systems directly predicts graphemes in the written domain , without using an external pronunciation lexicon , or a separate language model . We examine several sequence-to-sequence models including connectionist temporal classification ( CTC ) , the recurrent neural network ( RNN ) transducer , an attentionbased model , and a model which augments the RNN transducer with an attention mechanism . We find that the sequence-to-sequence models are competitive with traditional state-of-the-art approaches on dictation test sets , although the baseline , which uses a separate pronunciation and language model , outperforms these models on voice-search test sets .
Probabilistic models are a critical part of the modern deep learning toolbox - ranging from generative models ( VAEs , GANs ) , sequence to sequence models used in machine translation and speech processing to models over functional spaces ( conditional neural processes , neural processes ) . Given the size and complexity of these models , safely deploying them in applications requires the development of tools to analyze their behavior rigorously and provide some guarantees that these models are consistent with a list of desirable properties or specifications .
Our model first generates a set of talking point phrases as intermediate representation , followed by a separate decoder producing the final argument based on both input and the keyphrases . Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than a popular sequence-to-sequence generation model according to both automatic evaluation and human assessments .
In this article , we investigate whispered-to natural-speech conversion method using sequence to sequence generation approach by proposing modified transformer architecture . We investigate different kinds of features such as mel frequency cepstral coefficients ( MFCCs ) and smoothed spectral features .
MOTIVATION In recent years , several methods have been proposed for aligning two protein sequence profiles , with reported improvements in alignment accuracy and homolog discrimination versus sequence-sequence methods ( e.g. BLAST ) and profile-sequence methods ( e.g. PSI-BLAST ) . Profile-profile alignment is also the iterated step in progressive multiple sequence alignment algorithms such as CLUSTALW .
To our knowledge , this is the largest publicly available human labeled data set for open information extraction tasks . Using this labeled SAOKE data set , we train an end-to-end neural model using the sequence-to-sequence paradigm , called Logician , to transform sentences into facts . For each sentence , different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts , Logician performs a global optimization over all possible involved facts , in which facts not only compete with each other to attract the attention of words , but also cooperate to share words .
Synthesizing SQL queries from natural language is a long-standing open problem and has been attracting considerable interest recently . Toward solving the problem , the de facto approach is to employ a sequence-to-sequence-style model . Such an approach will necessarily require the SQL queries to be serialized . Since the same SQL query may have multiple equivalent serializations , training a sequence-to-sequence-style model is sensitive to the choice from one of them . This phenomenon is documented as the " order-matters " problem .
We explore a novel approach for Semantic Role Labeling ( SRL ) by casting it as a sequence-to-sequence process . We employ an attention-based model enriched with a copying mechanism to ensure faithful regeneration of the input sequence , while enabling interleaved generation of argument role labels .
Since our self-training does not change the model structure , it does not influence the efficiency of translation , such as the translation speed . The experimental results showed that the translation quality improved not only in the sequence-to-sequence ( seq-to-seq ) models but also in the transformer models .
This allows every position in the decoder to attend over all positions in the input sequence . This mimics the typical encoder - decoder attention mechanisms in sequence - to - sequence models such as wu2016google , bahdanau2014neural , JonasFaceNet2017 . The encoder contains self - attention layers .
This task can also be naturally cast as mapping an input sequence of words in a source document to a target sequence of words called summary . In the recent past , deep - learning based models that map an input sequence into another output sequence , called sequence - to - sequence models , have been successful in many problems such as machine translation , speech recognition and video captioning . In the framework of sequence - to - sequence models , a very relevant model to our task is the attentional Recurrent Neural Network ( RNN ) encoder - decoder model proposed in nmt , which has produced state - of - the - art performance in machine translation ( MT ) , which is also a natural language task .
The factorization turns the joint modeling problem into a sequence problem , where one learns to predict the next pixel given all the previously generated pixels . But to model the highly nonlinear and long - range correlations between pixels and the complex conditional distributions that result , a highly expressive sequence model is necessary . Recurrent Neural Networks ( RNN ) are powerful models that offer a compact , shared parametrization of a series of conditional distributions .
We find that the bare SPINN - PI - NT model performs little better than the RNN baseline , but that SPINN - PI with the added tracking LSTM performs well . The success of SPINN - PI , which is the hybrid tree - sequence model , suggests that the tree - and sequence - based encoding methods are at least partially complementary , with the sequence model presumably providing useful local word disambiguation . The full SPINN model with its relatively weak internal parser performs slightly less well , but nonetheless robustly exceeds the performance of the RNN baseline .
Our starting point is previous work on Graph Neural Networks scarselli2009graph , which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences . The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence - based models ( e.g. , LSTMs ) when the problem is graph - structured . We demonstrate the capabilities on some simple AI ( bAbI ) and graph algorithm learning tasks .
However , such methods may not perform well due to the loss of word order information . More recent models for distributed sentence representation fall into two categories according to the form of input sentence : sequence - based models and tree - structured models . Sequence - based models construct sentence representations from word sequences by taking in account the relationship between successive words .
The inputs to WaveNet ( linguistic features , predicted log fundamental frequency ( ) , and phoneme durations ) , however , require significant domain expertise to produce , involving elaborate text - analysis systems as well as a robust lexicon ( pronunciation guide ) . Tacotron , a sequence - to - sequence architecture for producing magnitude spectrograms from a sequence of characters , simplifies the traditional speech synthesis pipeline by replacing the production of these linguistic and acoustic features with a single neural network trained from data alone . To vocode the resulting magnitude spectrograms , Tacotron uses the Griffin - Lim algorithm for phase estimation , followed by an inverse short - time Fourier transform .
section : Conclusion This paper describes Tacotron 2 , a fully neural TTS system that combines a sequence - to - sequence recurrent network with attention to predicts mel spectrograms with a modified WaveNet vocoder . The resulting system synthesizes speech with Tacotron - level prosody and WaveNet - level audio quality .
Machine translation using deep neural networks achieved great success with sequence - to - sequence models that used recurrent neural networks ( RNNs ) with long short - term memory ( LSTM , ) cells . The basic sequence - to - sequence architecture is composed of an RNN encoder which reads the source sentence one token at a time and transforms it into a fixed - sized state vector . This is followed by an RNN decoder , which generates the target sentence , one token at a time , from the state vector .
In this paper , we propose a new method for parallel corpus mining based on multilingual sentence embeddings . Our approach uses a sequence - to - sequence architecture to train a multilingual sentence encoder on an initial parallel corpus , and a novel margin - based scoring method that overcomes the scale inconsistencies of cosine similarity . Our experiments show large improvements over previous methods in all the tested scenarios .
document : Graph2Seq : Graph to Sequence Learning with Attention - Based Neural Networks The celebrated Sequence to Sequence learning ( Seq2Seq ) technique and its numerous variants achieve excellent performance on many tasks . However , many machine learning tasks have inputs naturally represented as graphs ; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence .
In this section , we describe these representations of the SQL query . subsection : Sequence Representation We apply a simple template to construct the SQL query sequence : “ SELECT + aggregation function > + Split Symbol + selected column >
Unlike S - LSTM , ANF composes the child nodes attentively in respect to another relevant input vector . The input vector can be a learnable representation from a sequence representation . Given a matrix resulted by concatenating the child node representations , and the third input vector , ANF is defined as where is a learnable matrix , the attention score and the attention weight vector for each child .
section : Introduction Sequence to sequence learning has been successful in many tasks such as machine translation , speech recognition [ reference ] and text summarization [ reference ][ reference ][ reference ] amongst others . The dominant approach to date encodes the input sequence with a series of bi - directional recurrent neural networks ( RNN ) and generates a variable length output with another set of decoder RNNs , both of which interface via a soft - attention mechanism [ reference ] .
Therefore , in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses . We designed a sequence - to - sequence network composed of layer - normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training . We found that the knowledge of temporal consistency improves the best reported result on Human3.6 M dataset by approximately and helps our network to recover temporally consistent 3D poses over a sequence of images even when the 2D pose detector fails .
Our LSTM model significantly differs from all these models , as it makes no assumptions about the task . As a sequence - to - sequence prediction model it is somewhat related to the incremental parsing models , pioneered by and extended by . Such linear time parsers however typically need some task - specific constraints and might build up the parse in multiple passes .
In particular , there is no need for language features like part - of - speech tags or dependency parses . Furthermore , a generic sequence - to - sequence solution allows to extend the concept of capturing entailment across any sequential data , not only natural language . Recently , [ reference ] published
Second , an attentive hierarchical recurrent encoder ( AHRE ) is designed which is capable to encode sentences hierarchically and generate more descriptive representations by aggregation . Third , a new pooling method which combines multi-dimensional pooling and last-state pooling is used instead of the simple combination of max pooling and average pooling in the original ESIM . Last , a modification layer is added before the softmax layer to emphasize the importance of the last utterance in the context for response selection .
Decision-making algorithms are proposed for buyers , contractors , and subcontractors , each of which has a different optimization goal . We have built a wireless network simulator , called ESIM , to assist in the design and analysis of these algorithms . When the average communication energy required transferring a task is less than the average energy required to execute a task , our experimental results indicate that markets based on our protocol and decision-making algorithms fairly and effectively allocate energy resources among different tasks in both cooperative and competitive scenarios .
First , we train the word2vec model to transform the address records into their corresponding vector representations . Next , we apply the enhanced sequential inference model ( ESIM ) , a deep text-matching model , to make local and global inferences to determine if two addresses match . To evaluate the accuracy of the proposed method , we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods .
Moreover , current studies reveal contradictory findings and provide a fragmented understanding . The purpose of this paper is to set before the reader an ESIM adaptation framework underpinned by a morphogenetic approach . Our qualitative study resulted in a richer description of ESIM and its adaptation .
Moreover , current studies reveal contradictory findings and provide a fragmented understanding . The purpose of this paper is to set before the reader an ESIM adaptation framework underpinned by a morphogenetic approach . Our qualitative study resulted in a richer description of ESIM and its adaptation .
The ECM Signal Monitor ( ESIM ) , which was designed and constructed to monitor the IF and baseband outputs of wideband digital radios , is described . The ESIM system utilizes the wideband radio outputs to determine if the monitored radio is being jammed , and characterizes the type and intensity of the jamming , if present . The hardware and software elements of the ESIM are described , as are the ESIM jammer detection and characterization algorithms .
The embodiment of the invention discloses an ESIM number writing method which is used for improving the safety of writing an ESIM number and avoiding risks that the ESIM number is maliciously hijacked or tampered and the like . The embodiment method of the invention comprises the steps : a security system receives an ESIM number writing instruction ; if the safety system queries that the ESIM number writing instruction matches with a ESIM number server white list , the system sends an ESIM number writing request to a corresponding ESIM number server through a virtual private network ( VPN ) according to the ESIM number writing instruction ; the security system receives encrypted ESIM number data sent by the ESIM number server based on the ESIM number writing request ; the security decodes the encrypted ESIM number data ; and the security system writes the decrypted ESIM number data in ESIM equipment .
CED features a wide variety of indoor and outdoor scenes , which we hope will help drive forward event-based vision research . We also present an extension of the event camera simulator ESIM that enables simulation of color events . Finally , we present an evaluation of three state-of-the-art image reconstruction methods that can be used to convert the Color-DAVIS346 into a continuous-time , HDR , color video camera to visualise the event stream , and for use in downstream vision applications .
These dialog systems must learn to interact with external domain knowledge to achieve the desired goal e.g. recommending courses to a student , booking a table at a restaurant etc . This paper presents extended Enhanced Sequential Inference Model ( ESIM ) models : a ) K-ESIM ( Knowledge-ESIM ) , which incorporates the external domain knowledge and b ) T-ESIM ( Targeted-ESIM ) , which leverages information from similar conversations to improve the prediction accuracy . Our proposed models and the baseline ESIM model are evaluated on the Ubuntu and Advising datasets in the Sentence Selection track of the latest Dialog System Technology Challenge ( DSTC7 ) , where the goal is to find the correct next utterance , given a partial conversation , from a set of candidates .
For the document retrieval , we implemented a new entity linking approach . In order to be able to rank candidate facts and classify a claim on the basis of several selected facts , we introduce two extensions to the Enhanced LSTM ( ESIM ) .
To protect services that are already operating or plan to operate in the Ka ( 27.5 - 29.5 GHz ) band , it is very essential to assess the interference from earth stations in motion ( ESIMs ) if ESIMs are to be deployed in this band . This paper proposes a methodology and a scheme that can respectively be employed to evaluate and mitigate the interference from Maritime ESIM ( M-ESIM ) to three incumbent services ; point-to-point fixed service ( PTP-FS ) , point-to-multipoint fixed service ( PMP-FS ) and mobile service ( MS ) of the Ka band . Simulation is carried out taking into consideration M-ESIM ’s off-axis angles and the distance between M-ESIM and the incumbent services .
Based on the statistical analysis of flowrate in stream , a new type of index called ‘ Probabilistic Scheduling Index ’ was proposed in this work . The expert decision on a particular type of scheduling ( Fixed/Arranged/Demand ) was based on this index together with other site specific conditon The expert system ESIM was applied to an irrigation management problem of the Mae-Taeng Irrigation Project located in northern Thailand . Output of the expert system was found to be reasonable and demonstrated results could be used to assist in improving water management decisions .
Such models are general reading architectures . To demonstrate that our reading module can be integrated into arbitrary task architectures , we also add our refinement module to a reimplementation of a state of the art architecture for RTE called ESIM Chen2017_ESIM . We refer the interested reader to the ESIM paper for details of the model .
suppose , recognize , forget , remember , imagine , mean , agree , disagree , deny , promise . Table [ reference ] shows the frequency of aforementioned annotation tags in the SNLI test set along with the performance ( accuracy ) of ESIM , DR - BiLSTM ( Single ) , and DR - BiLSTM ( Ensemble ) . Table [ reference ] can be divided into four major categories : 1 ) gold label data , 2 ) word overlap , 3 ) sentence length , and 4 ) occurrence of special words .
• E1 . ESIM [ reference ] . We train the base ESIM model without the tree - LSTM , as in [ reference ] , on the task of natural language inference with ADAM for 10 epochs on MultiNLI and also SNLI . Training Schedules : We use the following batch size schedules
DBLP : conf / acl / ChenZLWJI17 proposed an enhanced sequential inference model ( ESIM ) , which is one of the best models so far and is used as one of our baselines in this paper . In this paper we enrich neural - network - based NLI models with external knowledge .
Our inference model converts the output hidden vectors of BiLSTMs to the fixed - length vector with pooling operations and puts it into the final classifier to determine the overall inference class . Particularly , in addition to using mean pooling and max pooling similarly to ESIM DBLP : conf / acl / ChenZLWJI17 , we propose to use weighted pooling based on external knowledge to obtain a fixed - length vector as in Equation ( [ reference ] ) ( [ reference ] ) . In our experiments , we regard the function as a 1 - layer feed - forward neural network with ReLU activation function .
Note that the above hyperparameter settings are same as those used in the baseline ESIM DBLP : conf / acl / ChenZLWJI17 model . ESIM is a strong NLI baseline framework with the source code made available at https: // github.com / lukecq1231 / nli ( the ESIM core code has also been adapted to summarization DBLP : conf / ijcai / ChenZLWJ16 and question - answering tasks Zhang : qa:2017 ) . The trade - off for calculating co - attention in Equation ( [ reference ] ) is selected in based on the development set .
The Stanford Natural Language Inference ( SNLI ) corpus snliemnlp2015 provides approximately 550 K hypothesis / premise pairs . Our baseline , the ESIM sequence model from Chen2017EnhancedLF , uses a biLSTM to encode the premise and hypothesis , followed by a matrix attention layer , a local inference layer , another biLSTM inference composition layer , and finally a pooling operation before the output layer . Overall , adding ELMo to the ESIM model improves accuracy by an average of 0.7 % across five random seeds .
We present Deep Voice , a production-quality text-to-speech system constructed entirely from deep neural networks . Deep Voice lays the groundwork for truly end-to-end neural speech synthesis . The system comprises five major building blocks : a segmentation model for locating phoneme boundaries , a grapheme-to-phoneme conversion model , a phoneme duration prediction model , a fundamental frequency prediction model , and an audio synthesis model .
This situation is changing with the deployment of deep neural networks into the synthesis process . Projects including WaveNet , Deep Voice , Voice Loop , and many others generate very natural and high-quality speech that may clone voice identity . We are slowly approaching an era where we will not be able to recognize a genuine voice from a synthesized one .
We present Deep Voice 3 , a fully-convolutional attention-based neural text-to-speech ( TTS ) system . Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster .
Scaling Text-to-Speech with Convolutional Sequence Learning . We present Deep Voice 3 , a fully-convolutional attention-based neural text-to-speech ( TTS ) system . Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster .
This paper examines some of the emerging technologies and speculates about the potentialities applications and digital convergence hold for online news edia . It examines five specific technologies – audio and video streaming , Third Voice , ultimedia digital editing software , Wireless Application Protocol with Cyberdisplay and mmersive digital videography . Exciting innovations , technologies and protocols under evelopment such as the prototypical virtual news reader , Ananova , V2ML , Bluetooth , ntelligent Equipment and Virtual Vision offer the promise of a wireless , streamlined lectronic news production process .
The worst-case alignment time is then computed for a gate using a precharacterization approach , requiring only eight sample points while maintaining a small error . The proposed methods were implemented in an industrial noise analysis tool called ClariNet . Results on industrial designs , including a large PPCmicroprocessor design , are presented to demonstrate the effectiveness of our approach .
Most modern text-to-speech architectures use a WaveNet vocoder for synthesizing high-fidelity waveform audio , but there have been limitations , such as high inference time , in its practical application due to its ancestral sampling scheme . The recently suggested Parallel WaveNet and ClariNet have achieved real-time audio synthesis capability by incorporating inverse autoregressive flow for parallel sampling . However , these approaches require a two-stage training pipeline with a well-trained teacher network and can only produce natural sound by using probability distillation along with auxiliary loss terms .
To model the unique characteristic of different voices , low dimensional trainable speaker embeddings are shared across each component of ClariNet and trained together with the rest of the model . We demonstrate that the multi-speaker ClariNet outperforms state-of-the-art systems in terms of naturalness , because the whole model is jointly optimized in an end-to-end manner .
When compared with the performance of C-MEX files generated by the MathWorks MATLAB compiler , we observed that , for our tests , the Fortran 90 programs ran faster than the corresponding C-MEX programs on an SGI Power Challenge and on a Sun SPARCstation 10 . This better performance is mainly attributed to our enhanced inference mechanism .
When compared with the performance of C-MEX files generated by the MathWorks MATLAB compiler , we observed that , for our tests , the Fortran 90 programs ran faster than the corresponding C-MEX programs on an SGI Power Challenge and on a Sun SPARCstation 10 . This better performance is mainly attributed to our enhanced inference mechanism . ftn$\sp1$MATLAB is a trademark of The MathWorks , Inc.
Based on the enhanced inference operator and the DR matrix , a new hybrid diagnostic method is established . Finally , a signal modulating circuit is used to verify the effectiveness of the enhanced inference operator on the D matrix , the R matrix and the DR matrix , which demonstrates the high efficiency of the enhanced inference operator and the feasibility of the new hybrid diagnostic method .
Although most of these models have claimed state-of-the-art performance , the original papers often reported on only one or two selected datasets . We provide a systematic study and show that ( i ) encoding contextual information by LSTM and inter-sentence interactions are critical , ( ii ) Tree-LSTM does not help as much as previously claimed but surprisingly improves performance on Twitter datasets , ( iii ) the Enhanced Sequential Inference Model is the best so far for larger datasets , while the Pairwise Word Interaction Model achieves the best performance when less data is available . We release our implementations as an open-source toolkit .
Our model consists of a universal layer shared for all tasks with several task-specific layers on top for each task . To generate universal representation , we employ the enhanced sequential inference model based on a deep learning and soft alignment techniques . The task-specific layers are composed of multilayer perceptrons .
And the label inference is based on the smoothness assumption of semi-supervised learning . In this study , we propose an enhanced label inference approach which incorporates the importance of each vertex into the existing inference algorithms to improve the prediction capabilities of the algorithms . We also present extensions of three algorithms which are capable of taking the vertex importance variable to apply in learning .
For that , model inference methods adapted to security testing can be used . This document tries to give some directions in order to combine enhanced model inference and model testing to ensure security of services automatically .
The extreme e ciency of sequential search and the natural tendency of tree prun ing systems to produce wide variations in workload partly explains why it is proving di cult to achieve more than e ciency for massively parallel implementations of the algorithm Here we introduce typical enhanced sequential algorithms and address the major issues of parallel game tree searching under conditions of severe pruning It is this pruning that makes the parallelization di cult After examining previous work on parallel algorithms we present a new method called Dynamic Multiple Principal Variation Splitting DM PVSplit and implement it on the AP In this algorithm high performance is achieved by using some novel approaches Paral lel speculative search of candidate principal variations is used to reduce re search delay and so obtain more quickly a better estimate of the subtree value This is achieved by con guring a at processor arrangement as a dynamically changeable tree structure Also with the aid of a group based scheduling strategy the game tree is split dynam ically at di erent levels This provides better load balance and takes more advantage of parallelism Preliminary experiments show that the scalability of the DM PVSplit algorithm is good for massively parallel machines
In this paper , we present a new state-of-the-art result , achieving the accuracy of 88.3 % on the standard benchmark , the Stanford Natural Language Inference dataset . This result is achieved first through our enhanced sequential encoding model , which outperforms the previous best model that employs more complicated network architectures , suggesting that the potential of sequential LSTM-based models have not been fully explored yet in previous work . We further show that by explicitly considering recursive architectures , we achieve additional improvement .
Compared with open domain data , clinical texts present unique linguistic phenomena , e.g. , a large number of medical terms and abbreviations , different written forms for the same medical concept , which make inference much harder . Incorporating domain-specific knowledge is a way to eliminate this problem , in this paper , we assemble a new incorporating medical concept definitions module on the classic enhanced sequential inference model ( ESIM ) , which first extracts the most relevant medical concept for each word , if it exists , then encodes the definition of this medical concept with a bidirectional long short-term network ( BiLSTM ) to obtain domain-specific definition representations , and attends these definition representations over vanilla word embeddings . The empirical evaluations are conducted to demonstrate that our model improves the prediction performance and achieves a high level of accuracy on the MedNLI dataset .
DBLP : conf / acl / ChenZLWJI17 proposed an enhanced sequential inference model ( ESIM ) , which is one of the best models so far and is used as one of our baselines in this paper . In this paper we enrich neural - network - based NLI models with external knowledge .
A Comparison of Sequence-to-Sequence Models for Speech Recognition Verification of deep probabilistic models Neural Argument Generation Augmented with Externally Retrieved Evidence WHALETRANS: E2E WHisper to nAturaL spEech conversion using modified TRANSformer network A comparison of scoring functions for protein sequence profile alignment Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning A Sequence-to-Sequence Model for Semantic Role Labeling NICT Self-Training Approach to Neural Machine Translation at NMT-2018 Attention is All you Need Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond Pixel Recurrent Neural Networks A Fast Unified Model for Parsing and Sentence Understanding Gated Graph Sequence Neural Networks A C-LSTM Neural Network for Text Classification Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions Depthwise Separable Convolutions for Neural Machine Translation Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks Neural Tree Indexers for Text Understanding Convolutional Sequence to Sequence Learning Exploiting Temporal Information for 3D Human Pose Estimation Grammar as a Foreign Language Reasoning about Entailment with Neural Attention Building Sequential Inference Models for End-to-End Response Selection DESP: a distributed economics-based subcontracting protocol for computation distribution in power-aware mobile ad hoc networks A deep learning architecture for semantic address matching Exploring the Adaptation of Enterprise Systems Implementation Methodology: A Morphogenetic Approach Exploring Enterprise Systems Implementation Methodology : A morphogenetic approach An ECM Monitor for Digital Communications ESIM number writing method, security system, ESIM number server and terminal CED: Color Event Camera Dataset Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification Methods to Evaluate and Mitigate the Interference from Maritime ESIM to Other Services in 27.5-29.5 GHz Band Expert system for irrigation management (ESIM) Dynamic Integration of Background Knowledge in Neural NLU Systems DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference Parameter Re-Initialization through Cyclical Batch Size Schedules Neural Natural Language Inference Models Enhanced with External Knowledge Deep contextualized word representations Deep Voice: Real-time Neural Text-to-Speech Deep Learning Serves Voice Cloning: How Vulnerable Are Automatic Speaker Veriﬁcation Systems to Spooﬁng Trials? Deep Voice 3: 2000-Speaker Neural Text-to-Speech Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning Online news: The changing digital mediascape Driver modeling and alignment for worst-case delay noise FloWaveNet : A Generative Flow for Raw Audio Multi-Speaker End-to-End Speech Synthesis Compiler techniques for MATLAB programs Compiler techniques for matlab programs A New Hybrid Fault Diagnostic Method for Combining Dependency Matrix Diagnosis and Fuzzy Diagnosis Based on an Enhanced Inference Operator Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering Multitask learning approach for understanding the relationship between two sentences Enhancing label inference algorithms considering vertex importance in graph-based semi-supervised learning Security Vulnerabilities Detection Using Model Inference for Applications and Security Protocols Speculative Parallelism Improves Search Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference Incorporating Domain Knowledge into Natural Language Inference on Clinical Texts Neural Natural Language Inference Models Enhanced with External Knowledge
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INTERSPEECH WSDM Rep4NLP@ACL NMT@ACL NIPS CoNLL ICML ACL ICLR ArXiv 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ICLR ACL ArXiv EACL ICML ECCV NIPS ICLR ICIS ICTC NAACL-HLT ArXiv ACL NAACL-HLT ICPR ICST Workshops ACL
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