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EMNLP Findings2021

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 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 zeroshot setting and can provide an additional loss to boost performance for EQA.

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Builds on6

  1. Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
  2. FQuAD: French Question Answering DatasetMartin d'Hoffschmidt, Wacim Belblidia, Quentin Heinrich, Tom Brendlé et al.EMNLP Findings 2020 · 65 citations
  3. Harvesting and Refining Question-Answer Pairs for Unsupervised QAZhongli Li, Wenhui Wang, Li Dong, Furu Wei et al.ACL 2020 · 29 citations
  4. Capturing Sentence Relations for Answer Sentence Selection with Multi-Perspective Graph EncodingZhixing Tian, Yuanzhe Zhang, Xinwei Feng, Wenbin Jiang et al.AAAI 2020 · 10 citations
  5. Span Selection Pre-training for Question AnsweringMichael R. Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto et al.ACL 2020 · 9 citations

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