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- W3100532709 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." @default.
- W3100532709 created "2020-11-23" @default.
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- W3100532709 date "2020-01-01" @default.
- W3100532709 modified "2023-09-24" @default.
- W3100532709 title "Probing Task-Oriented Dialogue Representation from Language Models" @default.
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- W3100532709 doi "https://doi.org/10.18653/v1/2020.emnlp-main.409" @default.
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