Stochastic Answer Networks for SQuAD 2.0
This work addresses the challenge of distinguishing answerable from unanswerable questions in reading comprehension for NLP researchers, but it is incremental as it builds on an existing model.
The paper tackles the problem of machine reading comprehension by extending the Stochastic Answer Network (SAN) to handle unanswerable questions in SQuAD 2.0, achieving results competitive with state-of-the-art models.
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.