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.

We extract data from the WMT'14 English-French (En-Fr) and English-German (En-De) datasets.  
 Question: Are this techniques used in training multilingual models, on what languages?
Abstractive

For instance, we observed that winter words were together and far away from summer ones. Week days were grouped as well and far from week-end days. 
 Question: What geometric properties do embeddings display?
Abstractive

 BIBREF3 also use bilingual crowd workers, but the studies supporting the use of crowdsourcing for MT evaluation were performed with older MT systems, and their findings may not carry over to the evaluation of contemporary higher-quality neural machine translation (NMT) systems. In addition, the MT developers to which crowd workers were compared are usually not professional translators.  We hypothesise that an evaluation of sentences in isolation, as applied by BIBREF3, precludes raters from detecting translation errors that become apparent only when inter-sentential context is available, and that they will judge MT quality less favourably when evaluating full documents. BIBREF3 used all source texts of the WMT 2017 Chinese-English test set for their experiments, of which only half were originally written in Chinese;  
 Question: What was the weakness in Hassan et al's evaluation design?

Abstractive