EQuANt extends QANet to SQuAD 2, achieving nearly twice the performance of a lightweight QANet baseline while also improving SQuAD 1.1 results via multi-task learning.
Stochastic Answer Networks for SQuAD 2.0
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abstract
This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is unanswerable or not. The extended SAN contains two components: a span detector and a binary classifier for judging whether the question is unanswerable, and both components are jointly optimized. Experiments show that SAN achieves the results competitive to the state-of-the-art on Stanford Question Answering Dataset (SQuAD) 2.0. To facilitate the research on this field, we release our code: https://github.com/kevinduh/san_mrc.
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cs.CL 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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EQuANt (Enhanced Question Answer Network)
EQuANt extends QANet to SQuAD 2, achieving nearly twice the performance of a lightweight QANet baseline while also improving SQuAD 1.1 results via multi-task learning.