# Stochastic Answer Networks for SQuAD 2.0

arXiv: https://arxiv.org/abs/1809.09194 · HTML: https://mlsanity.com/paper/1809.09194/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking](https://mlsanity.com/paper/2107.12578/) (2021)
- [To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning](https://mlsanity.com/paper/2208.01299/) (2022)
- [Semantics-Preserved Distortion for Personal Privacy Protection in Information Management](https://mlsanity.com/paper/2201.00965/) (2022)
- [Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension](https://mlsanity.com/paper/2208.08750/) (2022)
- [Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2301.00429/) (2023)

## Abstract (arXiv)

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.