Analyzing The Forgetting Problem In The Pretrain-finetuning Of Dialogue Response Models

He Tianxing, Liu Jun, Cho Kyunghyun, Ott Myle, Liu Bing, Glass James, Peng Fuchun. EACL 2019

[Paper]    
Model Architecture Pretraining Methods Survey Paper Tools Training Techniques Transformer

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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