Publication
AutoEQA: Auto-Encoding Questions for Extractive Question Answering
Stalin Varanasi; Saadullah Amin; Günter Neumann
In: The 2021 Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing (EMNLP-2021), Findings of EMNLP, November 7-11, Punta Cana, Dominican Republic, The Association for Computational Linguistics, 209 N. Eighth Street Stroudsburg, PA 18360 USA, 11/2021.
Abstract
There has been a significant progress in the field of extractive question
answering (EQA) in the recent years. However, most of them rely 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 (QA) task
during encoding and a question generation (QG) task during decoding. Our method
performs well in a zero-shot setting and can provide an additional
loss to boost performance for EQA.