EMNLP 2021finding5 citations
AutoEQA: Auto-Encoding Questions for Extractive Question Answering
Stalin Varanasi, Saadullah Amin, Guenter Neumann
Abstract
There has been a significant progress in the field of Extractive Question Answering (EQA) in the recent years. However, most of them are reliant on annotations of answer-spans in the corresponding passages. In this work, we address the problem of EQA when no annotations are present for the answer span, i.e., when the dataset contains only questions and corresponding passages. Our method is based on auto-encoding of the question that performs a question answering task during encoding and a question generation task during decoding. We show that our method performs well in a zero-shot setting and can provide an additional loss to boost performance for EQA.
BibTeX
@inproceedings{varanasi-etal-2021-autoeqa-auto,
title = "{A}uto{EQA}: Auto-Encoding Questions for Extractive Question Answering",
author = "Varanasi, Stalin and
Amin, Saadullah and
Neumann, Guenter",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.403/",
doi = "10.18653/v1/2021.findings-emnlp.403",
pages = "4706--4712"
}