In this task, you will be presented with a context from an academic paper and a question based on the context. You have to classify the questions into "Extractive", "Abstractive", or "Yes-no" questions. Extractive questions can be answered by concatenating extracts taken from a context into a summary while answering abstractive questions involves paraphrasing the context using novel sentences. Yes-no question is a question whose expected answer is one of two choices, one that affirms the question and one that denies the question. Typically, the choices are either yes or no.

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-no
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Q: In our setup, our starting point is a base model, trained on NLI data. Rather than employing automated adversarial methods, here the model's “adversary” is a human annotator. Given a context (also often called a “premise” in NLI), and a desired target label, we ask the human writer to provide a hypothesis that fools the model into misclassifying the label. One can think of the writer as a “white hat” hacker, trying to identify vulnerabilities in the system. For each human-generated example that is misclassified, we also ask the writer to provide a reason why they believe it was misclassified. 
 Question: Do they use active learning to create their dataset?

A: Yes-no
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Q: We use three datasets for our experiments: IWSLT14 German-English, Turkish-English, and WMT14 English-German. 
 Question: what datasets were used?

A:
Abstractive
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