@inproceedings{xu-etal-2019-neural,
title = "Neural Response Generation with Meta-words",
author = "Xu, Can and
Wu, Wei and
Tao, Chongyang and
Hu, Huang and
Schuerman, Matt and
Wang, Ying",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1538",
doi = "10.18653/v1/P19-1538",
pages = "5416--5426",
abstract = "We present open domain dialogue generation with meta-words. A meta-word is a structured record that describes attributes of a response, and thus allows us to explicitly model the one-to-many relationship within open domain dialogues and perform response generation in an explainable and controllable manner. To incorporate meta-words into generation, we propose a novel goal-tracking memory network that formalizes meta-word expression as a goal in response generation and manages the generation process to achieve the goal with a state memory panel and a state controller. Experimental results from both automatic evaluation and human judgment on two large-scale data sets indicate that our model can significantly outperform state-of-the-art generation models in terms of response relevance, response diversity, and accuracy of meta-word expression.",
}
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<abstract>We present open domain dialogue generation with meta-words. A meta-word is a structured record that describes attributes of a response, and thus allows us to explicitly model the one-to-many relationship within open domain dialogues and perform response generation in an explainable and controllable manner. To incorporate meta-words into generation, we propose a novel goal-tracking memory network that formalizes meta-word expression as a goal in response generation and manages the generation process to achieve the goal with a state memory panel and a state controller. Experimental results from both automatic evaluation and human judgment on two large-scale data sets indicate that our model can significantly outperform state-of-the-art generation models in terms of response relevance, response diversity, and accuracy of meta-word expression.</abstract>
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%0 Conference Proceedings
%T Neural Response Generation with Meta-words
%A Xu, Can
%A Wu, Wei
%A Tao, Chongyang
%A Hu, Huang
%A Schuerman, Matt
%A Wang, Ying
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F xu-etal-2019-neural
%X We present open domain dialogue generation with meta-words. A meta-word is a structured record that describes attributes of a response, and thus allows us to explicitly model the one-to-many relationship within open domain dialogues and perform response generation in an explainable and controllable manner. To incorporate meta-words into generation, we propose a novel goal-tracking memory network that formalizes meta-word expression as a goal in response generation and manages the generation process to achieve the goal with a state memory panel and a state controller. Experimental results from both automatic evaluation and human judgment on two large-scale data sets indicate that our model can significantly outperform state-of-the-art generation models in terms of response relevance, response diversity, and accuracy of meta-word expression.
%R 10.18653/v1/P19-1538
%U https://aclanthology.org/P19-1538
%U https://doi.org/10.18653/v1/P19-1538
%P 5416-5426
Markdown (Informal)
[Neural Response Generation with Meta-words](https://aclanthology.org/P19-1538) (Xu et al., ACL 2019)
ACL
- Can Xu, Wei Wu, Chongyang Tao, Huang Hu, Matt Schuerman, and Ying Wang. 2019. Neural Response Generation with Meta-words. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5416–5426, Florence, Italy. Association for Computational Linguistics.