You will be given a definition of a task first, then some input of the task.
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.

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?