@inproceedings{qian-etal-2022-capturing,
title = "Capturing Conversational Interaction for Question Answering via Global History Reasoning",
author = "Qian, Jin and
Zou, Bowei and
Dong, Mengxing and
Li, Xiao and
Aw, AiTi and
Hong, Yu",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.159",
doi = "10.18653/v1/2022.findings-naacl.159",
pages = "2071--2078",
abstract = "Conversational Question Answering (ConvQA) is required to answer the current question, conditioned on the observable paragraph-level context and conversation history. Previous works have intensively studied history-dependent reasoning. They perceive and absorb topic-related information of prior utterances in the interactive encoding stage. It yielded significant improvement compared to history-independent reasoning. This paper further strengthens the ConvQA encoder by establishing long-distance dependency among global utterances in multi-turn conversation. We use multi-layer transformers to resolve long-distance relationships, which potentially contribute to the reweighting of attentive information in historical utterances. Experiments on QuAC show that our method obtains a substantial improvement (1{\%}), yielding the F1 score of 73.7{\%}. All source codes are available at \url{https://github.com/jaytsien/GHR}.",
}
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<abstract>Conversational Question Answering (ConvQA) is required to answer the current question, conditioned on the observable paragraph-level context and conversation history. Previous works have intensively studied history-dependent reasoning. They perceive and absorb topic-related information of prior utterances in the interactive encoding stage. It yielded significant improvement compared to history-independent reasoning. This paper further strengthens the ConvQA encoder by establishing long-distance dependency among global utterances in multi-turn conversation. We use multi-layer transformers to resolve long-distance relationships, which potentially contribute to the reweighting of attentive information in historical utterances. Experiments on QuAC show that our method obtains a substantial improvement (1%), yielding the F1 score of 73.7%. All source codes are available at https://github.com/jaytsien/GHR.</abstract>
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%0 Conference Proceedings
%T Capturing Conversational Interaction for Question Answering via Global History Reasoning
%A Qian, Jin
%A Zou, Bowei
%A Dong, Mengxing
%A Li, Xiao
%A Aw, AiTi
%A Hong, Yu
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F qian-etal-2022-capturing
%X Conversational Question Answering (ConvQA) is required to answer the current question, conditioned on the observable paragraph-level context and conversation history. Previous works have intensively studied history-dependent reasoning. They perceive and absorb topic-related information of prior utterances in the interactive encoding stage. It yielded significant improvement compared to history-independent reasoning. This paper further strengthens the ConvQA encoder by establishing long-distance dependency among global utterances in multi-turn conversation. We use multi-layer transformers to resolve long-distance relationships, which potentially contribute to the reweighting of attentive information in historical utterances. Experiments on QuAC show that our method obtains a substantial improvement (1%), yielding the F1 score of 73.7%. All source codes are available at https://github.com/jaytsien/GHR.
%R 10.18653/v1/2022.findings-naacl.159
%U https://aclanthology.org/2022.findings-naacl.159
%U https://doi.org/10.18653/v1/2022.findings-naacl.159
%P 2071-2078
Markdown (Informal)
[Capturing Conversational Interaction for Question Answering via Global History Reasoning](https://aclanthology.org/2022.findings-naacl.159) (Qian et al., Findings 2022)
ACL