In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Input: Consider Input: Evaluating FastText, GloVe and word2vec, we show that compared to other word representation learning algorithms, the FastText performs best.

Output: What do you use to calculate word/sub-word embeddings


Input: Consider Input: Inspired by the supervised reordering in conventional SMT, in this paper, we propose a Supervised Attention based NMT (SA-NMT) model. Specifically, similar to conventional SMT, we first run off-the-shelf aligners (GIZA++ BIBREF3 or fast_align BIBREF4 etc.) to obtain the alignment of the bilingual training corpus in advance. Then, treating this alignment result as the supervision of attention, we jointly learn attention and translation, both in supervised manners. Since the conventional aligners delivers higher quality alignment, it is expected that the alignment in the supervised attention NMT will be improved leading to better end-to-end translation performance.

Output: Which conventional alignment models do they use as guidance?


Input: Consider Input: The participants are asked to score each summary on three indicators: relevance, conciseness and readability on a 1 to 5 scale, and rank the summary pair (tie allowed). We observe that SPNet reaches the highest score in both ROUGE and CIC


Output: What automatic and human evaluation metrics are used to compare SPNet to its counterparts?