Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality

Pei Zhou, Hyundong Cho, Pegah Jandaghi, Dong-Ho Lee, Bill Yuchen Lin, Jay Pujara, Xiang Ren


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.
Anthology ID:
2022.emnlp-main.714
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10450–10468
Language:
URL:
https://aclanthology.org/2022.emnlp-main.714
DOI:
10.18653/v1/2022.emnlp-main.714
Bibkey:
Cite (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.
Cite (Informal):
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality (Zhou et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.714.pdf
Software:
 2022.emnlp-main.714.software.zip
Dataset:
 2022.emnlp-main.714.dataset.zip