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        <title>Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models</title>
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        <namePart type="given">Qingyang</namePart>
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    <name type="personal">
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    <abstract>Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks. The recent success of large pre-trained language models such as BERT and GPT-2 (Devlin et al., 2019; Radford et al., 2019) have suggested the effectiveness of incorporating language priors in down-stream NLP tasks. However, how much pre-trained language models can help dialog response generation is still under exploration. In this paper, we propose a simple, general, and effective framework: Alternating Recurrent Dialog Model (ARDM). ARDM models each speaker separately and takes advantage of the large pre-trained language model. It requires no supervision from human annotations such as belief states or dialog acts to achieve effective conversations. ARDM outperforms or is on par with state-of-the-art methods on two popular task-oriented dialog datasets: CamRest676 and MultiWOZ. Moreover, we can generalize ARDM to more challenging, non-collaborative tasks such as persuasion. In persuasion tasks, ARDM is capable of generating human-like responses to persuade people to donate to a charity.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-main.110</identifier>
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%0 Conference Proceedings
%T Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models
%A Wu, Qingyang
%A Zhang, Yichi
%A Li, Yu
%A Yu, Zhou
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F wu-etal-2021-alternating
%X Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks. The recent success of large pre-trained language models such as BERT and GPT-2 (Devlin et al., 2019; Radford et al., 2019) have suggested the effectiveness of incorporating language priors in down-stream NLP tasks. However, how much pre-trained language models can help dialog response generation is still under exploration. In this paper, we propose a simple, general, and effective framework: Alternating Recurrent Dialog Model (ARDM). ARDM models each speaker separately and takes advantage of the large pre-trained language model. It requires no supervision from human annotations such as belief states or dialog acts to achieve effective conversations. ARDM outperforms or is on par with state-of-the-art methods on two popular task-oriented dialog datasets: CamRest676 and MultiWOZ. Moreover, we can generalize ARDM to more challenging, non-collaborative tasks such as persuasion. In persuasion tasks, ARDM is capable of generating human-like responses to persuade people to donate to a charity.
%R 10.18653/v1/2021.eacl-main.110
%U https://aclanthology.org/2021.eacl-main.110
%U https://doi.org/10.18653/v1/2021.eacl-main.110
%P 1292-1301

Markdown (Informal)

[Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models](https://aclanthology.org/2021.eacl-main.110) (Wu et al., EACL 2021)

• Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models (Wu et al., EACL 2021)

ACL

• Qingyang Wu, Yichi Zhang, Yu Li, and Zhou Yu. 2021. Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1292–1301, Online. Association for Computational Linguistics.