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Although exposure bias has been widely studied in some NLP tasks, it facesits unique challenges in dialogue response generation, the representativeone-to-various generation scenario. In real human dialogue, there are manyappropriate responses for the same context, not only with differentexpressions, but also with different topics. Therefore, due to the much biggergap between various ground-truth responses and the generated syntheticresponse, exposure bias is more challenging in dialogue generation task. What'smore, as MLE encourages the model to only learn the common words amongdifferent ground-truth responses, but ignores the interesting and specificparts, exposure bias may further lead to the common response generationproblem, such as "I don't know" and "HaHa?" In this paper, we propose a noveladaptive switching mechanism, which learns to automatically transit betweenground-truth learning and generated learning regarding the word-level matchingscore, such as the cosine similarity. Experimental results on both Chinese STCdataset and English Reddit dataset, show that our adaptive method achieves asignificant improvement in terms of metric-based evaluation and humanevaluation, as compared with the state-of-the-art exposure bias approaches.Further analysis on NMT task also shows that our model can achieve asignificant improvement.

Adaptive Bridge between Training and Inference for Dialogue