@inproceedings{luo-etal-2018-auto,
title = "An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation",
author = "Luo, Liangchen and
Xu, Jingjing and
Lin, Junyang and
Zeng, Qi and
Sun, Xu",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1075",
doi = "10.18653/v1/D18-1075",
pages = "702--707",
abstract = "Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs. To address this problem, we propose an Auto-Encoder Matching (AEM) model to learn such dependency. The model contains two auto-encoders and one mapping module. The auto-encoders learn the semantic representations of inputs and responses, and the mapping module learns to connect the utterance-level representations. Experimental results from automatic and human evaluations demonstrate that our model is capable of generating responses of high coherence and fluency compared to baseline models.",
}
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<abstract>Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs. To address this problem, we propose an Auto-Encoder Matching (AEM) model to learn such dependency. The model contains two auto-encoders and one mapping module. The auto-encoders learn the semantic representations of inputs and responses, and the mapping module learns to connect the utterance-level representations. Experimental results from automatic and human evaluations demonstrate that our model is capable of generating responses of high coherence and fluency compared to baseline models.</abstract>
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%0 Conference Proceedings
%T An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
%A Luo, Liangchen
%A Xu, Jingjing
%A Lin, Junyang
%A Zeng, Qi
%A Sun, Xu
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F luo-etal-2018-auto
%X Generating semantically coherent responses is still a major challenge in dialogue generation. Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs. To address this problem, we propose an Auto-Encoder Matching (AEM) model to learn such dependency. The model contains two auto-encoders and one mapping module. The auto-encoders learn the semantic representations of inputs and responses, and the mapping module learns to connect the utterance-level representations. Experimental results from automatic and human evaluations demonstrate that our model is capable of generating responses of high coherence and fluency compared to baseline models.
%R 10.18653/v1/D18-1075
%U https://aclanthology.org/D18-1075
%U https://doi.org/10.18653/v1/D18-1075
%P 702-707
Markdown (Informal)
[An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation](https://aclanthology.org/D18-1075) (Luo et al., EMNLP 2018)
ACL