In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Example Input: We use datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks BIBREF7, BIBREF19, BIBREF20. Given a question and a cluster of newswire documents, the contestants were asked to generate a 250-word summary answering the question. DUC-05 contains 1,600 summaries (50 questions x 32 systems); in DUC-06, 1,750 summaries are included (50 questions x 35 systems); and DUC-07 has 1,440 summaries (45 questions x 32 systems).
Question: What dataset do they use?
Example Output: datasets from the NIST DUC-05, DUC-06 and DUC-07 shared tasks

Example Input: In our experiments, we used WordNet 3.0 BIBREF9 as our external knowledge base INLINEFORM0 . For word embeddings, we experimented with two popular models: (1) GloVe embeddings trained by BIBREF10 on Wikipedia and Gigaword 5 (vocab: 400K, dim: 300), and (2) w2v-gn, Word2vec BIBREF5 trained on the Google News dataset (vocab: 3M, dim: 300). Our coverage enhancement starts by transforming the knowledge base INLINEFORM0 into a vector space representation that is comparable to that of the corpus-based space INLINEFORM1 . To this end, we use two techniques for learning low-dimensional feature spaces from knowledge graphs: DeepWalk and node2vec. DeepWalk uses a stream of short random walks in order to extract local information for a node from the graph. By treating these walks as short sentences and phrases in a special language, the approach learns latent representations for each node. Similarly, node2vec learns a mapping of nodes to continuous vectors that maximizes the likelihood of preserving network neighborhoods of nodes. Thanks to a flexible objective that is not tied to a particular sampling strategy, node2vec reports improvements over DeepWalk on multiple classification and link prediction datasets. For both these systems we used the default parameters and set the dimensionality of output representation to 100. Also, note than nodes in the semantic graph of WordNet represent synsets. Hence, a polysemous word would correspond to multiple nodes. In our experiments, we use the MaxSim assumption of BIBREF11 in order to map words to synsets.
Question: What other embedding models are tested?
Example Output: GloVe embeddings trained by BIBREF10 on Wikipedia and Gigaword 5 (vocab: 400K, dim: 300) w2v-gn, Word2vec BIBREF5 trained on the Google News dataset (vocab: 3M, dim: 300) DeepWalk node2vec

Example Input: Corpus Embedding
Suppose that INLINEFORM0 is the target low-resource corpus, we are interested in optimizing the acoustic model with a much larger training corpora set INLINEFORM1 where INLINEFORM2 is the number of corpora and INLINEFORM3 . Each corpus INLINEFORM4 is a collection of INLINEFORM5 pairs where INLINEFORM6 is the input features and INLINEFORM7 is its target.

Our purpose here is to compute the embedding INLINEFORM0 for each corpus INLINEFORM1 where INLINEFORM2 is expected to encode information about its corpus INLINEFORM3 . Those embeddings can be jointly trained with the standard multilingual model BIBREF4 . First, the embedding matrix INLINEFORM4 for all corpora is initialized, the INLINEFORM5 -th row of INLINEFORM6 is corresponding to the embedding INLINEFORM7 of the corpus INLINEFORM8 . Next, during the training phase, INLINEFORM9 can be used to bias the input feature INLINEFORM10 as follows. DISPLAYFORM0

where INLINEFORM0 is an utterance sampled randomly from INLINEFORM1 , INLINEFORM2 is its hidden features, INLINEFORM3 is the parameter of the acoustic model and Encoder is the stacked bidirectional LSTM as shown in Figure. FIGREF5 . Next, we apply the language specific softmax to compute logits INLINEFORM4 and optimize them with the CTC objective BIBREF29 . The embedding matrix INLINEFORM5 can be optimized together with the model during the training process.
Question: How do they compute corpus-level embeddings?
Example Output:
First, the embedding matrix INLINEFORM4 for all corpora is initialized during the training phase, INLINEFORM9 can be used to bias the input feature Next, we apply the language specific softmax to compute logits INLINEFORM4 and optimize them with the CTC objective