Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems
Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, Rui Yan · 2017
The study on human-computer conversation systems is a hot research topic nowadays.One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks.However, the standard Seq2Seq model is prone to generate trivial responses.In this paper, we aim to generate a more meaningful and informative reply when answering a given question.We propose an implicit content-introducing method which incorporates additional information into the Se-q2Seq model in a flexible way.Specifically, we fuse the general decoding and the auxiliary cue word information through our proposed hierarchical gated fusion unit.Experiments on real-life data demonstrate that our model consistently outperforms a set of competitive baselines in terms of BLEU scores and human evaluation.