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        <title>Probing Task-Oriented Dialogue Representation from Language Models</title>
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        <namePart type="given">Chien-Sheng</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>This paper investigates pre-trained language models to find out which model intrinsically carries the most informative representation for task-oriented dialogue tasks. We approach the problem from two aspects: supervised classifier probe and unsupervised mutual information probe. We fine-tune a feed-forward layer as the classifier probe on top of a fixed pre-trained language model with annotated labels in a supervised way. Meanwhile, we propose an unsupervised mutual information probe to evaluate the mutual dependence between a real clustering and a representation clustering. The goals of this empirical paper are to 1) investigate probing techniques, especially from the unsupervised mutual information aspect, 2) provide guidelines of pre-trained language model selection for the dialogue research community, 3) find insights of pre-training factors for dialogue application that may be the key to success.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.409</identifier>
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%0 Conference Proceedings
%T Probing Task-Oriented Dialogue Representation from Language Models
%A Wu, Chien-Sheng
%A Xiong, Caiming
%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 wu-xiong-2020-probing
%X This paper investigates pre-trained language models to find out which model intrinsically carries the most informative representation for task-oriented dialogue tasks. We approach the problem from two aspects: supervised classifier probe and unsupervised mutual information probe. We fine-tune a feed-forward layer as the classifier probe on top of a fixed pre-trained language model with annotated labels in a supervised way. Meanwhile, we propose an unsupervised mutual information probe to evaluate the mutual dependence between a real clustering and a representation clustering. The goals of this empirical paper are to 1) investigate probing techniques, especially from the unsupervised mutual information aspect, 2) provide guidelines of pre-trained language model selection for the dialogue research community, 3) find insights of pre-training factors for dialogue application that may be the key to success.
%R 10.18653/v1/2020.emnlp-main.409
%U https://aclanthology.org/2020.emnlp-main.409
%U https://doi.org/10.18653/v1/2020.emnlp-main.409
%P 5036-5051

Markdown (Informal)

[Probing Task-Oriented Dialogue Representation from Language Models](https://aclanthology.org/2020.emnlp-main.409) (Wu & Xiong, EMNLP 2020)

• Probing Task-Oriented Dialogue Representation from Language Models (Wu & Xiong, EMNLP 2020)

ACL

• Chien-Sheng Wu and Caiming Xiong. 2020. Probing Task-Oriented Dialogue Representation from Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5036–5051, Online. Association for Computational Linguistics.