In this paper, we study the problem of data augmentation for language
understanding in task-oriented dialogue system. In contrast to previous work
which augments an utterance without considering its relation with other
utterances, we propose a sequence-to-sequence generation based data
augmentation framework that leverages one utterance's same semantic
alternatives in the training data. A novel diversity rank is incorporated into
the utterance representation to make the model produce diverse utterances and
these diversely augmented utterances help to improve the language understanding
module. Experimental results on the Airline Travel Information System dataset
and a newly created semantic frame annotation on Stanford Multi-turn,
Multidomain Dialogue Dataset show that our framework achieves significant
improvements of 6.38 and 10.04 F-scores respectively when only a training set
of hundreds utterances is represented. Case studies also confirm that our
method generates diverse utterances.

本研究针对面向任务对话系统中语言理解的数据增强问题，提出了基于序列到序列生成的数据增强框架来提高模型的性能，该框架利用训练数据中某个话语的同义替代品来增强数据，将多样性排名纳入话语表示中，生成多样性增加的话语，实验证明在仅有数百个话语的情况下，在 Airline Travel Information System 数据集和 Stanford Multi-turn，Multidomain Dialogue 数据集上都取得了显著的 6.38 和 10.04 分数提高。