Generating Informative Responses with Controlled Sentence Function

Pei Ke, Jian Guan, Minlie Huang, Xiaoyan Zhu


Abstract
Sentence function is a significant factor to achieve the purpose of the speaker, which, however, has not been touched in large-scale conversation generation so far. In this paper, we present a model to generate informative responses with controlled sentence function. Our model utilizes a continuous latent variable to capture various word patterns that realize the expected sentence function, and introduces a type controller to deal with the compatibility of controlling sentence function and generating informative content. Conditioned on the latent variable, the type controller determines the type (i.e., function-related, topic, and ordinary word) of a word to be generated at each decoding position. Experiments show that our model outperforms state-of-the-art baselines, and it has the ability to generate responses with both controlled sentence function and informative content.
Anthology ID:
P18-1139
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1499–1508
Language:
URL:
https://aclanthology.org/P18-1139
DOI:
10.18653/v1/P18-1139
Bibkey:
Cite (ACL):
Pei Ke, Jian Guan, Minlie Huang, and Xiaoyan Zhu. 2018. Generating Informative Responses with Controlled Sentence Function. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1499–1508, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Generating Informative Responses with Controlled Sentence Function (Ke et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1139.pdf
Poster:
 P18-1139.Poster.pdf
Code
 kepei1106/SentenceFunction