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arXiv · 1910.07117

Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models

Tianxing He·Jun Liu·Kyunghyun Cho·Myle Ott·Bing Liu·James Glass·Fuchun Peng
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Abstract

In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. Our major finding is that after standard finetuning, the model forgets some of the important language generation skills acquired during large-scale pretraining. We demonstrate the forgetting phenomenon through a set of detailed behavior analysis from the perspectives of knowledge transfer, context sensitivity, and function space projection. As a preliminary attempt to alleviate the forgetting problem, we propose an intuitive finetuning strategy named "mix-review". We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent. Finally, we discuss interesting behavior of the resulting dialogue model and its implications.

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Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James Glass, Fuchun Peng. 2019-10-16. Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models. https://arxiv.org/abs/1910.07117

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