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.

Ex 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.

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


Ex Input:
The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences.

Ex Output:
How big is dataset used to train Word2Vec for the Italian Language?


Ex Input:
We have observed that the number of epochs is an important parameter and its increase leads to results that rank our two worst models almost equal, or even better than others.

Ex Output:

How does different parameter settings impact the performance and semantic capacity of resulting model?


