@inproceedings{pan-etal-2019-improving,
title = "Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration",
author = "Pan, Zhufeng and
Bai, Kun and
Wang, Yan and
Zhou, Lianqiang and
Liu, Xiaojiang",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1191",
doi = "10.18653/v1/D19-1191",
pages = "1824--1833",
abstract = "In multi-turn dialogue, utterances do not always take the full form of sentences. These incomplete utterances will greatly reduce the performance of open-domain dialogue systems. Restoring more incomplete utterances from context could potentially help the systems generate more relevant responses. To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context. We also propose a {``}pick-and-combine{''} model to restore the incomplete utterance from its context. Experimental results demonstrate that the annotated dataset and the proposed approach significantly boost the response quality of both single-turn and multi-turn dialogue systems.",
}
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<abstract>In multi-turn dialogue, utterances do not always take the full form of sentences. These incomplete utterances will greatly reduce the performance of open-domain dialogue systems. Restoring more incomplete utterances from context could potentially help the systems generate more relevant responses. To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context. We also propose a “pick-and-combine” model to restore the incomplete utterance from its context. Experimental results demonstrate that the annotated dataset and the proposed approach significantly boost the response quality of both single-turn and multi-turn dialogue systems.</abstract>
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%0 Conference Proceedings
%T Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration
%A Pan, Zhufeng
%A Bai, Kun
%A Wang, Yan
%A Zhou, Lianqiang
%A Liu, Xiaojiang
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F pan-etal-2019-improving
%X In multi-turn dialogue, utterances do not always take the full form of sentences. These incomplete utterances will greatly reduce the performance of open-domain dialogue systems. Restoring more incomplete utterances from context could potentially help the systems generate more relevant responses. To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context. We also propose a “pick-and-combine” model to restore the incomplete utterance from its context. Experimental results demonstrate that the annotated dataset and the proposed approach significantly boost the response quality of both single-turn and multi-turn dialogue systems.
%R 10.18653/v1/D19-1191
%U https://aclanthology.org/D19-1191
%U https://doi.org/10.18653/v1/D19-1191
%P 1824-1833
Markdown (Informal)
[Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration](https://aclanthology.org/D19-1191) (Pan et al., EMNLP-IJCNLP 2019)
ACL