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]: The corpus consists of 53 documents, which contain an average number of 156.1 sentences per document, each with 19.55 tokens on average.
Question: How many documents are in the new corpus?
[A]: Extractive

[Q]: Secondly, a well-known problem of uneven yet unknown distribution of word senses is alleviated by modifying a naive Bayesian classifier. Thanks to this correction, the classifier is no longer biased towards senses that have more training data. Finally, the aggregated feature values corresponding to target word senses are used to build a naive Bayesian classifier adjusted to a situation of unknown a priori probabilities.
Question: How do they deal with unknown distribution senses?
[A]: Abstractive

[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