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        <namePart type="given">Xiang</namePart>
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    <abstract>Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions. Current conversational models are increasingly capable of producing turns that are context-relevant, but in order to produce compelling agents, these models need to be able to predict and optimize for turns that are genuinely engaging. We leverage social media feedback data (number of replies and upvotes) to build a large-scale training dataset for feedback prediction. To alleviate possible distortion between the feedback and engagingness, we convert the ranking problem to a comparison of response pairs which involve few confounding factors. We trained DialogRPT, a set of GPT-2 based models on 133M pairs of human feedback data and the resulting ranker outperformed several baselines. Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback. We finally combine the feedback prediction models and a human-like scoring model to rank the machine-generated dialog responses. Crowd-sourced human evaluation shows that our ranking method correlates better with real human preferences than baseline models.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.28</identifier>
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%0 Conference Proceedings
%T Dialogue Response Ranking Training with Large-Scale Human Feedback Data
%A Gao, Xiang
%A Zhang, Yizhe
%A Galley, Michel
%A Brockett, Chris
%A Dolan, Bill
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F gao-etal-2020-dialogue
%X Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions. Current conversational models are increasingly capable of producing turns that are context-relevant, but in order to produce compelling agents, these models need to be able to predict and optimize for turns that are genuinely engaging. We leverage social media feedback data (number of replies and upvotes) to build a large-scale training dataset for feedback prediction. To alleviate possible distortion between the feedback and engagingness, we convert the ranking problem to a comparison of response pairs which involve few confounding factors. We trained DialogRPT, a set of GPT-2 based models on 133M pairs of human feedback data and the resulting ranker outperformed several baselines. Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback. We finally combine the feedback prediction models and a human-like scoring model to rank the machine-generated dialog responses. Crowd-sourced human evaluation shows that our ranking method correlates better with real human preferences than baseline models.
%R 10.18653/v1/2020.emnlp-main.28
%U https://aclanthology.org/2020.emnlp-main.28
%U https://doi.org/10.18653/v1/2020.emnlp-main.28
%P 386-395

Markdown (Informal)

[Dialogue Response Ranking Training with Large-Scale Human Feedback Data](https://aclanthology.org/2020.emnlp-main.28) (Gao et al., EMNLP 2020)

• Dialogue Response Ranking Training with Large-Scale Human Feedback Data (Gao et al., EMNLP 2020)

ACL

• Xiang Gao, Yizhe Zhang, Michel Galley, Chris Brockett, and Bill Dolan. 2020. Dialogue Response Ranking Training with Large-Scale Human Feedback Data. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 386–395, Online. Association for Computational Linguistics.