Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems
Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, Rui Yan
Abstract
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 Seq2Seq 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.- Anthology ID:
- D17-1233
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2190–2199
- Language:
- URL:
- https://aclanthology.org/D17-1233
- DOI:
- 10.18653/v1/D17-1233
- Cite (ACL):
- Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, and Rui Yan. 2017. Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2190–2199, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems (Yao et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D17-1233.pdf