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EMNLP Findings2021

AutoEQA: Auto-Encoding Questions for Extractive Question Answering

Stalin Varanasi, Saadullah Amin, Guenter Neumann

摘要

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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它引用的 Paper6

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

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