Semantic vector similarity and topical distribution similarity were explored for ASE . Integer linear programming strategy was used for ASF , which combined dependencies with the language model , based on word importance . As a first step towards the new challenge , we obtained some encouraging results on actual exam questions in Chinese subject 's RC tasks of Beijing Gaokao , which helped us obtain insights into techniques needed to solve real-word complex questions . We trace the enculturation trajectories of employees in a midsized technology firm based on analyses of 10.24 million internal emails . Our language-based model of changing cultural fit ( 1 ) predicts individual attainment ; ( 2 ) reveals distinct patterns of adaptation for employees who exit voluntarily , exit involuntarily , and remain employed ; ( 3 ) demonstrates that rapid early cultural adaptation reduces the risk of involuntary , but not voluntary , exit ; and ( 4 ) finds ... As a practical and theoretical motivation , probabilistic finite automata ( PFAs ) have been shown to exhibit a stochastic behavior in many problems such as determining the reliability of sequential circuits . The research on PFAs almost always concentrates on the language-based approach : [ 1 ] considered the supervisory control of probabilistic discrete event systems modeled by PFAs , and [ 2 ] considered the state estimation of PFAs , which was a natural generalization of the detectability of finite automata . More recently , a matrix-based approach to mixvalued logical networks based on the semi-tensor product ( STP ) of matrices was proposed by Cheng et al. [ 3 ] . We have taken up one of the most relevant issues in modern translation studies — definition of translation as process . We have also analysed language means used to build TRANSLATION GAME mental space , as well as key aspects of activities performed by the translator , who is the key figure of the process . Special attention is paid to risk , which is the most important element of the translator ’s episteme and the determining factor for the intentional horizon of the translator . Atomic deferral is a language-level mechanism for transactional memory ( TM ) that enables programmers to move output and long-running operations out of a transaction 's body without sacrificing serializability : the deferred operation appears to execute as part of its parent transaction , even though it does not make use of TM . We introduce the first implementation of atomic deferral , based on transaction-friendly locks ; describe enhancements to its API ; and demonstrate its effectiveness . a by-product of language usage , without the intended purpose of learning a particular linguistic feature ” ( Schmitt , 2010 ) . This byproduct of language usage can occur when , for example , when reading a novel simply for pleasure . The key point of IVA is that the focus of attention is on the meaning of the text . Further , a common assumption within the field of conceptual modeling is that conceptual models and information systems describe entities in the real world , outside the systems . An alternative view is provided by an ontological commitment that recognizes that the institutional world is constructed through language use and the creation of institutional facts . Such an ontological commitment implies that there is an institutional reality , which , to a great extent , is constructed using information infrastructures . A " test of concept " prototype of this model shows that small neural networks which receive direct ( not preprocessed ) digitized sound signals can learn to recognize phonemes effectively and quickly . The model provides a theoretical outline for expanding this prototype to a language using system . But , after becoming familiar with the structure of the language , they eventually discover its rules and how the various parts are interrelated ( Krashen , 1994 ) . Functions of Language Language is a system of signals , including voice sounds , gestures or written symbols which encodes and decodes information . The goal of language is to communicate meaning . For example , in speech recognition , we want to find a cross - domain mapping from a sequence of phonemes to a sequence of characters . By optimizing the ODM cost function in odm ] Equation [ reference ] , we can find a smooth function that takes phonemes at its input and outputs a sequence of characters that respects the language model . However , the main problem with this method is that the network can learn to ignore part of the input distribution and still satisfy the ODM cost function by its output distribution . KN : The Kneser - Ney ( KN ) model is a widely used language model proposed by [ reference ] . We use the KenLM toolkit to train 5 - gram models without pruning . Deep gated convolutional networks have been proved to be very effective in single channel speech separation . However current state-of-the-art framework often considers training the gated convolutional networks in time-frequency ( TF ) domain . Such an approach will result in limited perceptual score , such as signal-to-distortion ratio ( SDR ) upper bound of separated utterances and also fail to exploit an end-to-end framework . We hope to take advantages of both these two methods . In this paper we present a gated convolutional approach to low resource speech recognition tasks . The gated convolutional networks use convolutional architectures to learn input features and a gate to control information . Experiments are conducted on the OpenKWS , a series of low resource keyword search evaluations . In this paper , however , we propose StackReader , an end-to-end neural network model , to solve this problem , without recurrent neural network ( RNN ) units and its variants . This simple model is based solely on attention mechanism and gated convolutional neural network . Experiments on SQuAD have shown to have relatively high accuracy with a significant decrease in training time . In this paper we introduce new gated convolutional networks and apply them to SER , which can be more efficient since they allow parallelization over sequential tokens . We present a novel model architecture that incorporates a gated convolutional neural network and a temporal attention-based localization method for speech emotion recognition . To the best of the authors ' knowledge , this is the first time that such a hybrid architecture is employed for SER . First , a few models that permit multi-time step parallel processing are evaluated . The models evaluated include Gated ConvNet , Diagonal LSTM , and QRNN ( quasi RNN ) . Since the performance of these models are not as good as the LSTM , one-dimensional depthwise convolution is added to improve the performance . The one-dimensional convolution helps finding the temporal patterns of speech signal . Second , Simple Gated Convolution Network ( Simple Gated ConvNet ) is proposed for improved performance when the parameter count is very small . The Simple Gated ConvNet employs the simplest form of Gated ConvNet . Instead it relies on one-dimensional The input layer produces representations based on various features , such as part-of-speech and relation embeddings . Gated convolutional layer , the key component of our model , extracts n-gram features at different granularities and models the interactions between different texts ( questions , answers , and passages ) . Bilinear interactions are used as output layer to capture the relations among the final expressive representations and to produce the final answers . With pre-trained word embeddings from a large external corpus , DAZER is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space . The relevance signals are extracted through a gated convolutional process . The gate mechanism controls which convolution filters output the relevance signals in a category dependent manner . Specifically , we firstly adopt three parallel gated fully convolutional blocks to extract spatiotemporal features from visual , acoustic and textual modalities of micro-videos . Then , an additional gated fully convolutional block is used to fuse such three modalities of spatiotemporal features . Finally , corresponding prototype is simultaneously learned to improve the robustness against softmax classification function . All of which show that effective combination of the global structural properties and local fine - grained details can achieve complementary advantages . Therefore , to fully explore the global and local clues , in this paper , we propose a novel full convolutional network named as CoupleNet , to couple the global structure and local parts to boost the detection accuracy . Specifically , the object proposals obtained by the RPN are fed into the coupling module which consists of two branches . 1 . We propose a unified fully convolutional network to jointly learn the local , global and context information for object detection . 2 . We start from a baseline setting of our FeatMap - Net ( “ FullyConvNet Baseline ” in the result table ) , for which multi - scale and sliding pooling is removed . This baseline setting is the conventional fully convolution network for segmentation , which can be considered as our implementation of the FCN method in . The result shows that our CNN baseline implementation ( “ FullyConvNet ” ) achieves very similar performance ( slightly better ) than the FCN method . This is turned into a distribution via a softmax : . Our encoder and decoder use gated convolutional neural networks which enable fast and accurate generation . Fast generation is essential to efficiently train on the model output as is done in this work as sequence - level losses require generating at training time . Some of them carry unsupervised learning on structures by making composition operations soft . To the best of our knowledge , gated recursive convolutional neural network ( grConv ) [ reference ] ) is the first model of its kind and used as an encoder for neural machine translation . The grConv architecture uses gating mechanism to control the information flow from children to parent . For example , SegNet utilizes the saved pool indices to recover the reduced spatial information . U - net uses the skip connection , while the Global Convolutional Network adapts the large kernel size . Besides , LRR adds the Laplacian Pyramid Reconstruction network , while RefineNet utilizes multi - path refinement network . Fully convolutional neural machine translation without this bottleneck was first achieved in and . The model in ( Extended Neural GPU ) used a recurrent stack of gated convolutional layers , while the model in ( ByteNet ) did away with recursion and used left - padded convolutions in the decoder . This idea , introduced in WaveNet , significantly improves efficiency of the model . ( 1 ) Two neural layers with Bidirectional Long Short-Term Memory ( BiLSTM ) are employed to learn representations of reviews . ( 2 ) Cross-View Training ( CVT ) is used to improve the representation learning over a small set of labeled reviews and a large set of unlabeled reviews from the same domain in a unified end-to-end architecture . ( 3 ) Since past nearby information in a text provides important semantic contexts for a prediction task in aspect mining , a moving-window attention component is proposed in EMOVA to enhance prediction accuracy . However , the supervised models only learn from task-specific labeled data during the main training phase . We therefore propose Cross-View Training ( CVT ) , a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data . On labeled examples , standard supervised learning is used . Discriminative and representative patches are collected for filter learning . Second , patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees , and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative . Third , filter responses are integrated with patch matching scores in RankSVM training . Our work aims to reduce the annotation effort involved via semisupervised learning . We propose an end-toend SRL model and demonstrate it can effectively leverage unlabeled data under the crossview training modeling paradigm . Our LSTMbased semantic role labeler is jointly trained with a sentence learner , which performs POS tagging , dependency parsing , and predicate identification which we argue are critical to learning directly from unlabeled data without recourse to external pre-processing tools . So , the Students do n’t get the knowledge about the interview process until they attend some interviews that is form the chances they miss . The solution is providing prior knowledge starting from the first year through an interview training system so that they can get the required skills . A major concern in a web application is its loading time that is addresses by using json data format . By learning to extrapolate cross-view motions , the representation can capture view-invariant motion dynamics which is discriminative for the action . In addition , we propose a view-adversarial training method to enhance learning of view-invariant features . We demonstrate the effectiveness of the learned representations for action recognition on multiple datasets . document : Semi - Supervised Sequence Modeling with Cross - View Training kevclark@cs.stanford.edu , thangluong@google.com , manning@cs.stanford.edu , qvl@google.com Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models , mainly because they can take advantage of large amounts of unlabeled text .
Semantic vector similarity and topical distribution similarity were explored for ASE . Integer linear programming strategy was used for ASF , which combined dependencies with the language model , based on word importance . As a first step towards the new challenge , we obtained some encouraging results on actual exam questions in Chinese subject 's RC tasks of Beijing Gaokao , which helped us obtain insights into techniques needed to solve real-word complex questions .
We trace the enculturation trajectories of employees in a midsized technology firm based on analyses of 10.24 million internal emails . Our language-based model of changing cultural fit ( 1 ) predicts individual attainment ; ( 2 ) reveals distinct patterns of adaptation for employees who exit voluntarily , exit involuntarily , and remain employed ; ( 3 ) demonstrates that rapid early cultural adaptation reduces the risk of involuntary , but not voluntary , exit ; and ( 4 ) finds ...
As a practical and theoretical motivation , probabilistic finite automata ( PFAs ) have been shown to exhibit a stochastic behavior in many problems such as determining the reliability of sequential circuits . The research on PFAs almost always concentrates on the language-based approach : [ 1 ] considered the supervisory control of probabilistic discrete event systems modeled by PFAs , and [ 2 ] considered the state estimation of PFAs , which was a natural generalization of the detectability of finite automata . More recently , a matrix-based approach to mixvalued logical networks based on the semi-tensor product ( STP ) of matrices was proposed by Cheng et al. [ 3 ] .
We have taken up one of the most relevant issues in modern translation studies — definition of translation as process . We have also analysed language means used to build TRANSLATION GAME mental space , as well as key aspects of activities performed by the translator , who is the key figure of the process . Special attention is paid to risk , which is the most important element of the translator ’s episteme and the determining factor for the intentional horizon of the translator .
Atomic deferral is a language-level mechanism for transactional memory ( TM ) that enables programmers to move output and long-running operations out of a transaction 's body without sacrificing serializability : the deferred operation appears to execute as part of its parent transaction , even though it does not make use of TM . We introduce the first implementation of atomic deferral , based on transaction-friendly locks ; describe enhancements to its API ; and demonstrate its effectiveness .
a by-product of language usage , without the intended purpose of learning a particular linguistic feature ” ( Schmitt , 2010 ) . This byproduct of language usage can occur when , for example , when reading a novel simply for pleasure . The key point of IVA is that the focus of attention is on the meaning of the text .
Further , a common assumption within the field of conceptual modeling is that conceptual models and information systems describe entities in the real world , outside the systems . An alternative view is provided by an ontological commitment that recognizes that the institutional world is constructed through language use and the creation of institutional facts . Such an ontological commitment implies that there is an institutional reality , which , to a great extent , is constructed using information infrastructures .
A " test of concept " prototype of this model shows that small neural networks which receive direct ( not preprocessed ) digitized sound signals can learn to recognize phonemes effectively and quickly . The model provides a theoretical outline for expanding this prototype to a language using system .
But , after becoming familiar with the structure of the language , they eventually discover its rules and how the various parts are interrelated ( Krashen , 1994 ) . Functions of Language Language is a system of signals , including voice sounds , gestures or written symbols which encodes and decodes information . The goal of language is to communicate meaning .
For example , in speech recognition , we want to find a cross - domain mapping from a sequence of phonemes to a sequence of characters . By optimizing the ODM cost function in odm ] Equation [ reference ] , we can find a smooth function that takes phonemes at its input and outputs a sequence of characters that respects the language model . However , the main problem with this method is that the network can learn to ignore part of the input distribution and still satisfy the ODM cost function by its output distribution .
KN : The Kneser - Ney ( KN ) model is a widely used language model proposed by [ reference ] . We use the KenLM toolkit to train 5 - gram models without pruning .
Deep gated convolutional networks have been proved to be very effective in single channel speech separation . However current state-of-the-art framework often considers training the gated convolutional networks in time-frequency ( TF ) domain . Such an approach will result in limited perceptual score , such as signal-to-distortion ratio ( SDR ) upper bound of separated utterances and also fail to exploit an end-to-end framework .
We hope to take advantages of both these two methods . In this paper we present a gated convolutional approach to low resource speech recognition tasks . The gated convolutional networks use convolutional architectures to learn input features and a gate to control information . Experiments are conducted on the OpenKWS , a series of low resource keyword search evaluations .
In this paper , however , we propose StackReader , an end-to-end neural network model , to solve this problem , without recurrent neural network ( RNN ) units and its variants . This simple model is based solely on attention mechanism and gated convolutional neural network . Experiments on SQuAD have shown to have relatively high accuracy with a significant decrease in training time .
In this paper we introduce new gated convolutional networks and apply them to SER , which can be more efficient since they allow parallelization over sequential tokens . We present a novel model architecture that incorporates a gated convolutional neural network and a temporal attention-based localization method for speech emotion recognition . To the best of the authors ' knowledge , this is the first time that such a hybrid architecture is employed for SER .
First , a few models that permit multi-time step parallel processing are evaluated . The models evaluated include Gated ConvNet , Diagonal LSTM , and QRNN ( quasi RNN ) . Since the performance of these models are not as good as the LSTM , one-dimensional depthwise convolution is added to improve the performance . The one-dimensional convolution helps finding the temporal patterns of speech signal . Second , Simple Gated Convolution Network ( Simple Gated ConvNet ) is proposed for improved performance when the parameter count is very small . The Simple Gated ConvNet employs the simplest form of Gated ConvNet . Instead it relies on one-dimensional
The input layer produces representations based on various features , such as part-of-speech and relation embeddings . Gated convolutional layer , the key component of our model , extracts n-gram features at different granularities and models the interactions between different texts ( questions , answers , and passages ) . Bilinear interactions are used as output layer to capture the relations among the final expressive representations and to produce the final answers .
With pre-trained word embeddings from a large external corpus , DAZER is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space . The relevance signals are extracted through a gated convolutional process . The gate mechanism controls which convolution filters output the relevance signals in a category dependent manner .
Specifically , we firstly adopt three parallel gated fully convolutional blocks to extract spatiotemporal features from visual , acoustic and textual modalities of micro-videos . Then , an additional gated fully convolutional block is used to fuse such three modalities of spatiotemporal features . Finally , corresponding prototype is simultaneously learned to improve the robustness against softmax classification function .
All of which show that effective combination of the global structural properties and local fine - grained details can achieve complementary advantages . Therefore , to fully explore the global and local clues , in this paper , we propose a novel full convolutional network named as CoupleNet , to couple the global structure and local parts to boost the detection accuracy . Specifically , the object proposals obtained by the RPN are fed into the coupling module which consists of two branches .
1 . We propose a unified fully convolutional network to jointly learn the local , global and context information for object detection . 2 .
We start from a baseline setting of our FeatMap - Net ( “ FullyConvNet Baseline ” in the result table ) , for which multi - scale and sliding pooling is removed . This baseline setting is the conventional fully convolution network for segmentation , which can be considered as our implementation of the FCN method in . The result shows that our CNN baseline implementation ( “ FullyConvNet ” ) achieves very similar performance ( slightly better ) than the FCN method .
This is turned into a distribution via a softmax : . Our encoder and decoder use gated convolutional neural networks which enable fast and accurate generation . Fast generation is essential to efficiently train on the model output as is done in this work as sequence - level losses require generating at training time .
Some of them carry unsupervised learning on structures by making composition operations soft . To the best of our knowledge , gated recursive convolutional neural network ( grConv ) [ reference ] ) is the first model of its kind and used as an encoder for neural machine translation . The grConv architecture uses gating mechanism to control the information flow from children to parent .
For example , SegNet utilizes the saved pool indices to recover the reduced spatial information . U - net uses the skip connection , while the Global Convolutional Network adapts the large kernel size . Besides , LRR adds the Laplacian Pyramid Reconstruction network , while RefineNet utilizes multi - path refinement network .
Fully convolutional neural machine translation without this bottleneck was first achieved in and . The model in ( Extended Neural GPU ) used a recurrent stack of gated convolutional layers , while the model in ( ByteNet ) did away with recursion and used left - padded convolutions in the decoder . This idea , introduced in WaveNet , significantly improves efficiency of the model .
( 1 ) Two neural layers with Bidirectional Long Short-Term Memory ( BiLSTM ) are employed to learn representations of reviews . ( 2 ) Cross-View Training ( CVT ) is used to improve the representation learning over a small set of labeled reviews and a large set of unlabeled reviews from the same domain in a unified end-to-end architecture . ( 3 ) Since past nearby information in a text provides important semantic contexts for a prediction task in aspect mining , a moving-window attention component is proposed in EMOVA to enhance prediction accuracy .
However , the supervised models only learn from task-specific labeled data during the main training phase . We therefore propose Cross-View Training ( CVT ) , a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data . On labeled examples , standard supervised learning is used .
Discriminative and representative patches are collected for filter learning . Second , patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees , and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative . Third , filter responses are integrated with patch matching scores in RankSVM training .
Our work aims to reduce the annotation effort involved via semisupervised learning . We propose an end-toend SRL model and demonstrate it can effectively leverage unlabeled data under the crossview training modeling paradigm . Our LSTMbased semantic role labeler is jointly trained with a sentence learner , which performs POS tagging , dependency parsing , and predicate identification which we argue are critical to learning directly from unlabeled data without recourse to external pre-processing tools .
So , the Students do n’t get the knowledge about the interview process until they attend some interviews that is form the chances they miss . The solution is providing prior knowledge starting from the first year through an interview training system so that they can get the required skills . A major concern in a web application is its loading time that is addresses by using json data format .
By learning to extrapolate cross-view motions , the representation can capture view-invariant motion dynamics which is discriminative for the action . In addition , we propose a view-adversarial training method to enhance learning of view-invariant features . We demonstrate the effectiveness of the learned representations for action recognition on multiple datasets .
document : Semi - Supervised Sequence Modeling with Cross - View Training kevclark@cs.stanford.edu , thangluong@google.com , manning@cs.stanford.edu , qvl@google.com Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models , mainly because they can take advantage of large amounts of unlabeled text .
A Pipeline Approach to Free-Description Question Answering in Chinese Gaokao Reading Comprehension Enculturation Trajectories: Language, Cultural Adaptation, and Individual Outcomes in Organizations Modeling and reachability of probabilistic finite automata based on semi-tensor product of matrices A Vision of Translation as Play and Game Brief Announcement: Extending Transactional Memory with Atomic Deferral Considerations when researching EFL vocabulary acquisition through Extensive Reading Institutional ontology for conceptual modeling A dialogic model for natural language cognition Linguistics across Cultures: The Impact of Culture on Second Language Learning. PixelGAN Autoencoders Table-to-text Generation by Structure-aware Seq2seq Learning Furcax: End-to-end Monaural Speech Separation Based on Deep Gated (De)convolutional Neural Networks with Adversarial Example Training Gated convolutional networks based hybrid acoustic models for low resource speech recognition StackReader: An RNN-Free Reading Comprehension Model Combining Gated Convolutional Networks and Self-Attention Mechanism for Speech Emotion Recognition On-device Efficient Acoustic Modeling with Simple Gated Convolutional Networks Gated Convolutional Networks for Commonsense Machine Comprehension A Deep Relevance Model for Zero-Shot Document Filtering Multi-modal sequence model with gated fully convolutional blocks for micro-video venue classification CoupleNet: Coupling Global Structure with Local Parts for Object Detection Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation Classical Structured Prediction Losses for Sequence to Sequence Learning Learning to Compose Task-Specific Tree Structures Learning a Discriminative Feature Network for Semantic Segmentation Depthwise Separable Convolutions for Neural Machine Translation EMOVA: A Semi-supervised End-to-End Moving-Window Attentive Framework for Aspect Mining Semi-Supervised Sequence Modeling with Cross-View Training Learning Mid-level Filters for Person Re-identification Semi-Supervised Semantic Role Labeling with Cross-View Training Career Skills Enhancement with Ajax and JSON Data Exchange Unsupervised Learning of View-invariant Action Representations Semi-Supervised Sequence Modeling with Cross-View Training
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SPAA NIPS AAAI ICASSP ASRU AAAI ACII Workshops ICONIP (1) ACL (1) 2017 IEEE International Conference on Computer Vision (ICCV) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NAACL-HLT AAAI 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICLR PAKDD (2) EMNLP CVPR EMNLP/IJCNLP (1) EMNLP
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