On Task-Adaptive Pretraining for Dialogue Response Selection

• URL: http://arxiv.org/abs/2210.04073v1
• Date: Sat, 8 Oct 2022 17:58:49 GMT
• Title: On Task-Adaptive Pretraining for Dialogue Response Selection
• Authors: Tzu-Hsiang Lin, Ta-Chung Chi, Anna Rumshisky
• Abstract summary: This paper aims to verify assumptions made in previous advancements and understand the source of improvements for dialogue response selection (DRS) We show that initializing with RoBERTa achieve similar performance as BERT, and anticipate+NSP can outperform all previously proposed TAP tasks. Additional analyses shows that the main source of improvements comes from the TAP step, and that the NSP task is crucial to DRS.
• Score: 9.502775168613589
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in dialogue response selection (DRS) are based on the \textit{task-adaptive pre-training (TAP)} approach, by first initializing their model with BERT~\cite{devlin-etal-2019-bert}, and adapt to dialogue data with dialogue-specific or fine-grained pre-training tasks. However, it is uncertain whether BERT is the best initialization choice, or whether the proposed dialogue-specific fine-grained learning tasks are actually better than MLM+NSP. This paper aims to verify assumptions made in previous works and understand the source of improvements for DRS. We show that initializing with RoBERTa achieve similar performance as BERT, and MLM+NSP can outperform all previously proposed TAP tasks, during which we also contribute a new state-of-the-art on the Ubuntu corpus. Additional analyses shows that the main source of improvements comes from the TAP step, and that the NSP task is crucial to DRS, different from common NLU tasks.

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