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        <title>Multi-Source Probing for Open-Domain Conversational Understanding</title>
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        <namePart type="given">Yuanxi</namePart>
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        <namePart type="family">Huang</namePart>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Dialogue comprehension and generation are vital to the success of open-domain dialogue systems. Although pre-trained generative conversation models have made significant progress in generating fluent responses, people have difficulty judging whether they understand and efficiently model the contextual information of the conversation. In this study, we propose a Multi-Source Probing (MSP) method to probe the dialogue comprehension abilities of open-domain dialogue models. MSP aggregates features from multiple sources to accomplish diverse task goals and conducts downstream tasks in a generative manner that is consistent with dialogue model pre-training to leverage model capabilities. We conduct probing experiments on seven tasks that require various dialogue comprehension skills, based on the internal representations encoded by dialogue models. Experimental results show that open-domain dialogue models can encode semantic information in the intermediate hidden states, which facilitates dialogue comprehension tasks. Models of different scales and structures possess different conversational understanding capabilities. Our findings encourage a comprehensive evaluation and design of open-domain dialogue models.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.769</identifier>
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%0 Conference Proceedings
%T Multi-Source Probing for Open-Domain Conversational Understanding
%A Li, Yuanxi
%A Zhou, Hao
%A Zhou, Jie
%A Huang, Minlie
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-multi-source-probing
%X Dialogue comprehension and generation are vital to the success of open-domain dialogue systems. Although pre-trained generative conversation models have made significant progress in generating fluent responses, people have difficulty judging whether they understand and efficiently model the contextual information of the conversation. In this study, we propose a Multi-Source Probing (MSP) method to probe the dialogue comprehension abilities of open-domain dialogue models. MSP aggregates features from multiple sources to accomplish diverse task goals and conducts downstream tasks in a generative manner that is consistent with dialogue model pre-training to leverage model capabilities. We conduct probing experiments on seven tasks that require various dialogue comprehension skills, based on the internal representations encoded by dialogue models. Experimental results show that open-domain dialogue models can encode semantic information in the intermediate hidden states, which facilitates dialogue comprehension tasks. Models of different scales and structures possess different conversational understanding capabilities. Our findings encourage a comprehensive evaluation and design of open-domain dialogue models.
%R 10.18653/v1/2023.emnlp-main.769
%U https://aclanthology.org/2023.emnlp-main.769
%U https://doi.org/10.18653/v1/2023.emnlp-main.769
%P 12491-12505

Markdown (Informal)

[Multi-Source Probing for Open-Domain Conversational Understanding](https://aclanthology.org/2023.emnlp-main.769) (Li et al., EMNLP 2023)

• Multi-Source Probing for Open-Domain Conversational Understanding (Li et al., EMNLP 2023)

ACL

• Yuanxi Li, Hao Zhou, Jie Zhou, and Minlie Huang. 2023. Multi-Source Probing for Open-Domain Conversational Understanding. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12491–12505, Singapore. Association for Computational Linguistics.