@inproceedings{he-etal-2020-quase,
title = "{Q}u{ASE}: Question-Answer Driven Sentence Encoding",
author = "He, Hangfeng and
Ning, Qiang and
Roth, Dan",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.772/",
doi = "10.18653/v1/2020.acl-main.772",
pages = "8743--8758",
abstract = "Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)? For example, \textit{can we use QAMR (Michael et al., 2017) to improve named entity recognition?} We suggest that simply further pre-training BERT is often not the best option, and propose the \textit{question-answer driven sentence encoding (QuASE)} framework. QuASE learns representations from QA data, using BERT or other state-of-the-art contextual language models. In particular, we observe the need to distinguish between two types of sentence encodings, depending on whether the target task is a single- or multi-sentence input; in both cases, the resulting encoding is shown to be an easy-to-use plugin for many downstream tasks. This work may point out an alternative way to supervise NLP tasks."
}
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<abstract>Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)? For example, can we use QAMR (Michael et al., 2017) to improve named entity recognition? We suggest that simply further pre-training BERT is often not the best option, and propose the question-answer driven sentence encoding (QuASE) framework. QuASE learns representations from QA data, using BERT or other state-of-the-art contextual language models. In particular, we observe the need to distinguish between two types of sentence encodings, depending on whether the target task is a single- or multi-sentence input; in both cases, the resulting encoding is shown to be an easy-to-use plugin for many downstream tasks. This work may point out an alternative way to supervise NLP tasks.</abstract>
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%0 Conference Proceedings
%T QuASE: Question-Answer Driven Sentence Encoding
%A He, Hangfeng
%A Ning, Qiang
%A Roth, Dan
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F he-etal-2020-quase
%X Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)? For example, can we use QAMR (Michael et al., 2017) to improve named entity recognition? We suggest that simply further pre-training BERT is often not the best option, and propose the question-answer driven sentence encoding (QuASE) framework. QuASE learns representations from QA data, using BERT or other state-of-the-art contextual language models. In particular, we observe the need to distinguish between two types of sentence encodings, depending on whether the target task is a single- or multi-sentence input; in both cases, the resulting encoding is shown to be an easy-to-use plugin for many downstream tasks. This work may point out an alternative way to supervise NLP tasks.
%R 10.18653/v1/2020.acl-main.772
%U https://aclanthology.org/2020.acl-main.772/
%U https://doi.org/10.18653/v1/2020.acl-main.772
%P 8743-8758
Markdown (Informal)
[QuASE: Question-Answer Driven Sentence Encoding](https://aclanthology.org/2020.acl-main.772/) (He et al., ACL 2020)
ACL
- Hangfeng He, Qiang Ning, and Dan Roth. 2020. QuASE: Question-Answer Driven Sentence Encoding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8743–8758, Online. Association for Computational Linguistics.