Similarity Based Label Smoothing For Dialogue Generation
This work addresses an incremental improvement in regularization for generative neural conversational systems, potentially benefiting researchers and practitioners in dialogue generation.
The paper tackles the problem of label smoothing in dialogue generation by proposing a similarity-based weighting method to replace the uniform distribution of incorrect targets with a semantic distribution, reporting significant performance gains on two standard open-domain dialogue corpora.
Generative neural conversational systems are generally trained with the objective of minimizing the entropy loss between the training "hard" targets and the predicted logits. Often, performance gains and improved generalization can be achieved by using regularization techniques like label smoothing, which converts the training "hard" targets to "soft" targets. However, label smoothing enforces a data independent uniform distribution on the incorrect training targets, which leads to an incorrect assumption of equi-probable incorrect targets for each correct target. In this paper we propose and experiment with incorporating data dependent word similarity based weighing methods to transforms the uniform distribution of the incorrect target probabilities in label smoothing, to a more natural distribution based on semantics. We introduce hyperparameters to control the incorrect target distribution, and report significant performance gains over networks trained using standard label smoothing based loss, on two standard open domain dialogue corpora.