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.

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?
Extractive

Concretely, we selected gold standards that fit our problem definition and were published in the years 2016 to 2019, have at least $(2019 - publication\ year) imes 20$ citations, and bucket them according to the answer selection styles as described in Section SECREF4
Question: What modern MRC gold standards are analyzed?
Extractive

LiLi should have the following capabilities:
Question: What are the components of the general knowledge learning engine?
Abstractive