@inproceedings{zhang-etal-2022-g4,
title = "G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents",
author = "Zhang, Shiwei and
Du, Yiyang and
Liu, Guanzhong and
Yan, Zhao and
Cao, Yunbo",
editor = "Feng, Song and
Wan, Hui and
Yuan, Caixia and
Yu, Han",
booktitle = "Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dialdoc-1.11",
doi = "10.18653/v1/2022.dialdoc-1.11",
pages = "108--114",
abstract = "Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task. Unlike previous works which treat document-grounded dialogue modeling as a machine reading comprehension task from single document, MultiDoc2Dial task faces challenges of both seeking information from multiple documents and generating conversation response simultaneously. This paper summarizes our entries to agent response generation subtask in MultiDoc2Dial dataset. We propose a three-stage solution, Grounding-guided goal-oriented dialogues generation(G4), which predicts groundings from retrieved passages to guide the generation of the final response. Our experiments show that G4 achieves SacreBLEU score of 31.24 and F1 score of 44.6 which is 60.7{\%} higher than the baseline model.",
}
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<abstract>Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task. Unlike previous works which treat document-grounded dialogue modeling as a machine reading comprehension task from single document, MultiDoc2Dial task faces challenges of both seeking information from multiple documents and generating conversation response simultaneously. This paper summarizes our entries to agent response generation subtask in MultiDoc2Dial dataset. We propose a three-stage solution, Grounding-guided goal-oriented dialogues generation(G4), which predicts groundings from retrieved passages to guide the generation of the final response. Our experiments show that G4 achieves SacreBLEU score of 31.24 and F1 score of 44.6 which is 60.7% higher than the baseline model.</abstract>
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%0 Conference Proceedings
%T G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents
%A Zhang, Shiwei
%A Du, Yiyang
%A Liu, Guanzhong
%A Yan, Zhao
%A Cao, Yunbo
%Y Feng, Song
%Y Wan, Hui
%Y Yuan, Caixia
%Y Yu, Han
%S Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhang-etal-2022-g4
%X Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task. Unlike previous works which treat document-grounded dialogue modeling as a machine reading comprehension task from single document, MultiDoc2Dial task faces challenges of both seeking information from multiple documents and generating conversation response simultaneously. This paper summarizes our entries to agent response generation subtask in MultiDoc2Dial dataset. We propose a three-stage solution, Grounding-guided goal-oriented dialogues generation(G4), which predicts groundings from retrieved passages to guide the generation of the final response. Our experiments show that G4 achieves SacreBLEU score of 31.24 and F1 score of 44.6 which is 60.7% higher than the baseline model.
%R 10.18653/v1/2022.dialdoc-1.11
%U https://aclanthology.org/2022.dialdoc-1.11
%U https://doi.org/10.18653/v1/2022.dialdoc-1.11
%P 108-114
Markdown (Informal)
[G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents](https://aclanthology.org/2022.dialdoc-1.11) (Zhang et al., dialdoc 2022)
ACL