Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models
This addresses the forgetting problem in dialogue response generation, which is incremental as it builds on existing pretrain-finetune methods.
The study investigates how finetuning in the pretrain-finetune framework causes dialogue response models to forget important language generation skills from pretraining, and proposes a 'mix-review' strategy that alleviates this forgetting to some extent.
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.