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.

[Q]: In this paper, we first highlight the importance of TV and radio broadcast as a source of data for ASR, and the potential impact it can have. We then perform a statistical analysis of gender representation in a data set composed of four state-of-the-art corpora of French broadcast, widely used within the speech community. Finally we question the impact of such a representation on the systems developed on this data, through the perspective of an ASR system. 
 Question: What tasks did they use to evaluate performance for male and female speakers?
[A]: ASR


[Q]:  Recurrent Neural Network (RNN) Language Models
are trained to output the probability distribution of the upcoming word given a context, without explicitly representing the structure of the context BIBREF9, BIBREF10. ActionLSTM
models the linearized bracketed tree structure of a sentence by learning to predict the next action required to construct a phrase-structure parse BIBREF16. Generative Recurrent Neural Network Grammars (RNNG)
jointly model the word sequence as well as the underlying syntactic structure BIBREF18. 
 Question: What are the baseline models?
[A]: Recurrent Neural Network (RNN) ActionLSTM Generative Recurrent Neural Network Grammars (RNNG)


[Q]: After using Kytea, Japanese texts are applied LSW algorithm to replace OOV words by their synonyms.  
 Question: Are synonymous relation taken into account in the Japanese-Vietnamese task?
[A]:
Yes