@inproceedings{wang-etal-2021-template,
title = "A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems",
author = "Wang, Dingmin and
Chen, Ziyao and
He, Wanwei and
Zhong, Li and
Tao, Yunzhe and
Yang, Min",
editor = "Feng, Song and
Reddy, Siva and
Alikhani, Malihe and
He, He and
Ji, Yangfeng and
Iyyer, Mohit and
Yu, Zhou",
booktitle = "Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.dialdoc-1.3",
doi = "10.18653/v1/2021.dialdoc-1.3",
pages = "18--28",
abstract = "Most existing neural network based task-oriented dialog systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid pointer network for knowledge-based task-oriented dialog systems, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a memory pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.",
}
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<abstract>Most existing neural network based task-oriented dialog systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid pointer network for knowledge-based task-oriented dialog systems, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a memory pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.</abstract>
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%0 Conference Proceedings
%T A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems
%A Wang, Dingmin
%A Chen, Ziyao
%A He, Wanwei
%A Zhong, Li
%A Tao, Yunzhe
%A Yang, Min
%Y Feng, Song
%Y Reddy, Siva
%Y Alikhani, Malihe
%Y He, He
%Y Ji, Yangfeng
%Y Iyyer, Mohit
%Y Yu, Zhou
%S Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-template
%X Most existing neural network based task-oriented dialog systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of words, usually suffering from instability and poor readability. Inspired by the traditional template-based generation approaches, we propose a template-guided hybrid pointer network for knowledge-based task-oriented dialog systems, which retrieves several potentially relevant answers from a pre-constructed domain-specific conversational repository as guidance answers, and incorporates the guidance answers into both the encoding and decoding processes. Specifically, we design a memory pointer network model with a gating mechanism to fully exploit the semantic correlation between the retrieved answers and the ground-truth response. We evaluate our model on four widely used task-oriented datasets, including one simulated and three manually created datasets. The experimental results demonstrate that the proposed model achieves significantly better performance than the state-of-the-art methods over different automatic evaluation metrics.
%R 10.18653/v1/2021.dialdoc-1.3
%U https://aclanthology.org/2021.dialdoc-1.3
%U https://doi.org/10.18653/v1/2021.dialdoc-1.3
%P 18-28
Markdown (Informal)
[A Template-guided Hybrid Pointer Network for Knowledge-based Task-oriented Dialogue Systems](https://aclanthology.org/2021.dialdoc-1.3) (Wang et al., dialdoc 2021)
ACL