  Although exposure bias has been widely studied in some NLP tasks, it faces
its unique challenges in dialogue response generation, the representative
one-to-various generation scenario. In real human dialogue, there are many
appropriate responses for the same context, not only with different
expressions, but also with different topics. Therefore, due to the much bigger
gap between various ground-truth responses and the generated synthetic
response, exposure bias is more challenging in dialogue generation task. What's
more, as MLE encourages the model to only learn the common words among
different ground-truth responses, but ignores the interesting and specific
parts, exposure bias may further lead to the common response generation
problem, such as "I don't know" and "HaHa?" In this paper, we propose a novel
adaptive switching mechanism, which learns to automatically transit between
ground-truth learning and generated learning regarding the word-level matching
score, such as the cosine similarity. Experimental results on both Chinese STC
dataset and English Reddit dataset, show that our adaptive method achieves a
significant improvement in terms of metric-based evaluation and human
evaluation, as compared with the state-of-the-art exposure bias approaches.
Further analysis on NMT task also shows that our model can achieve a
significant improvement.
