  While several state-of-the-art approaches to dialogue state tracking (DST)
have shown promising performances on several benchmarks, there is still a
significant performance gap between seen slot values (i.e., values that occur
in both training set and test set) and unseen ones (values that occur in
training set but not in test set). Recently, the copy-mechanism has been widely
used in DST models to handle unseen slot values, which copies slot values from
user utterance directly. In this paper, we aim to find out the factors that
influence the generalization ability of a common copy-mechanism model for DST.
Our key observations include: 1) the copy-mechanism tends to memorize values
rather than infer them from contexts, which is the primary reason for
unsatisfactory generalization performance; 2) greater diversity of slot values
in the training set increase the performance on unseen values but slightly
decrease the performance on seen values. Moreover, we propose a simple but
effective algorithm of data augmentation to train copy-mechanism models, which
augments the input dataset by copying user utterances and replacing the real
slot values with randomly generated strings. Users could use two
hyper-parameters to realize a trade-off between the performances on seen values
and unseen ones, as well as a trade-off between overall performance and
computational cost. Experimental results on three widely used datasets (WoZ
2.0, DSTC2, and Multi-WoZ 2.0) show the effectiveness of our approach.
