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: Analysis of challenges from UTD
Our system relies on the pseudotext produced by ZRTools (the only freely available UTD system we are aware of), which presents several challenges for MT.  Assigning wrong words to a cluster
Since UTD is unsupervised, the discovered clusters are noisy.  Splitting words across different clusters
Although most UTD matches are across speakers, recall of cross-speaker matches is lower than for same-speaker matches. As a result, the same word from different speakers often appears in multiple clusters, preventing the model from learning good translations. UTD is sparse, giving low coverage We found that the patterns discovered by ZRTools match only 28% of the audio. This low coverage reduces training data size, affects alignment quality, and adversely affects translation, which is only possible when pseudoterms are present. 
 Question: what challenges are identified?

A: Extractive
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Q: For experiments, we train and evaluate our models in the civil law system of mainland China. We collect and construct a large-scale real-world data set of INLINEFORM0 case documents that the Supreme People's Court of People's Republic of China has made publicly available 
 Question: what is the size of the real-world civil case dataset?

A: Abstractive
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Q: The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 .  
 Question: Where are the hotel reviews from?

A:
Extractive
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