@inproceedings{zhou-etal-2022-reflect,
title = "Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality",
author = "Zhou, Pei and
Cho, Hyundong and
Jandaghi, Pegah and
Lee, Dong-Ho and
Lin, Bill Yuchen and
Pujara, Jay and
Ren, Xiang",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.714",
doi = "10.18653/v1/2022.emnlp-main.714",
pages = "10450--10468",
abstract = "Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations. In this paper, we demonstrate that current response generation (RG) models produce generic and dull responses in dialogues because they act reflexively, failing to explicitly model CG, both due to the lack of CG in training data and the standard RG training procedure. We introduce Reflect, a dataset that annotates dialogues with explicit CG (materialized as inferences approximating shared knowledge and beliefs) and solicits 9k diverse human-generated responses each following one common ground. Using Reflect, we showcase the limitations of current dialogue data and RG models: less than half of the responses in current data is rated as high quality (sensible, specific, and interesting) and models trained using this data have even lower quality, while most Reflect responses are judged high quality. Next, we analyze whether CG can help models produce better quality responses by using Reflect CG to guide RG models. Surprisingly, we find that simply prompting GPT3 to {``}think{''} about CG generates 30{\%} more quality responses, showing promising benefits to integrating CG into the RG process.",
}
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<abstract>Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations. In this paper, we demonstrate that current response generation (RG) models produce generic and dull responses in dialogues because they act reflexively, failing to explicitly model CG, both due to the lack of CG in training data and the standard RG training procedure. We introduce Reflect, a dataset that annotates dialogues with explicit CG (materialized as inferences approximating shared knowledge and beliefs) and solicits 9k diverse human-generated responses each following one common ground. Using Reflect, we showcase the limitations of current dialogue data and RG models: less than half of the responses in current data is rated as high quality (sensible, specific, and interesting) and models trained using this data have even lower quality, while most Reflect responses are judged high quality. Next, we analyze whether CG can help models produce better quality responses by using Reflect CG to guide RG models. Surprisingly, we find that simply prompting GPT3 to “think” about CG generates 30% more quality responses, showing promising benefits to integrating CG into the RG process.</abstract>
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%0 Conference Proceedings
%T Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality
%A Zhou, Pei
%A Cho, Hyundong
%A Jandaghi, Pegah
%A Lee, Dong-Ho
%A Lin, Bill Yuchen
%A Pujara, Jay
%A Ren, Xiang
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhou-etal-2022-reflect
%X Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations. In this paper, we demonstrate that current response generation (RG) models produce generic and dull responses in dialogues because they act reflexively, failing to explicitly model CG, both due to the lack of CG in training data and the standard RG training procedure. We introduce Reflect, a dataset that annotates dialogues with explicit CG (materialized as inferences approximating shared knowledge and beliefs) and solicits 9k diverse human-generated responses each following one common ground. Using Reflect, we showcase the limitations of current dialogue data and RG models: less than half of the responses in current data is rated as high quality (sensible, specific, and interesting) and models trained using this data have even lower quality, while most Reflect responses are judged high quality. Next, we analyze whether CG can help models produce better quality responses by using Reflect CG to guide RG models. Surprisingly, we find that simply prompting GPT3 to “think” about CG generates 30% more quality responses, showing promising benefits to integrating CG into the RG process.
%R 10.18653/v1/2022.emnlp-main.714
%U https://aclanthology.org/2022.emnlp-main.714
%U https://doi.org/10.18653/v1/2022.emnlp-main.714
%P 10450-10468
Markdown (Informal)
[Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality](https://aclanthology.org/2022.emnlp-main.714) (Zhou et al., EMNLP 2022)
ACL
- Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, and Xiang Ren. 2022. Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10450–10468, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.