In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Ex Input:
For the overlapped speech recognition problem, the conditional independence assumption in the output label streams is still made as in Equation ( 5 ). Then the cross-entropy based PIT can be transformed to sequence discriminative criterion based PIT as below,

$$\begin{split} \mathcal {J}_{\text{SEQ-PIT}}=\sum _u \min _{s^{\prime }\in \mathbf {S}} \frac{1}{N} \sum _{n\in [1,N]}-\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)}) \end{split}$$ (Eq. 44)

Different from Equation ( 7 ), the best permutation is decided by $\mathcal {J}_{\text{SEQ}}(\mathbf {L}_{un}^{(s^{\prime })},\mathbf {L}_{un}^{(r)})$ , which is the sequence discriminative criterion of taking the $s^{\prime }$ -th permutation in $n$ -th output inference stream at utterance $u$ . Similar to CE-PIT, $\mathcal {J}_{\text{SEQ}}$ of all the permutations are calculated and the minimum permutation is taken to do the optimization.

Ex Output:
How is the discriminative training formulation different from the standard ones?


Ex Input:
We first describe our models individually and then the ensembling technique that we employ. In the following, MN denotes Memory Networks to encode conversational history, RCNN signify R-CNN for object level representations of an image, Wt represents additional linear layer in the decoder, and LF a late fusion mechanism as defined in BIBREF0.

Models ::: LF-RCNN
Late fusion encoder BIBREF0 with concatenated history. We use two-layered LSTMs with 512 hidden units for embedding questions and history. The object-level features are weighed using only question embeddings. The word embeddings from Glove vectors are frozen and are not fine-tuned. Figure FIGREF6 gives an overview of the architecture.

Models ::: MN-RCNN
Memory network encoder BIBREF0 with bi-directional GRUs and word embeddings fine-tuned. Object-level features are weighed by question and caption embedding. The rest of the scheme is same as above. (Figure FIGREF6)

Models ::: MN-RCNN-Wt
Same as above but with an additional linear layer applied to the dot product of candidate answer and encoder output, and gated using tanh function. Compare Figure FIGREF6 with Figure FIGREF6

Ex Output:
Which three discriminative models did they use?


Ex Input:
The regressor and classifier used Support Vector Regression (SVR) and Support Vector Classification (SVC) respectively. The table includes a neural regressor (NNR) and a neural classifier (NNC). The neural classifier is trained in two set ups: “NNC top 5” uses classification labels as described in Section SECREF3, and “NNC SU4 F1” uses the regression labels, that is, the ROUGE-SU4 F1 scores of each sentence.

Ex Output:

What classification approaches were experimented for this task?


