@inproceedings{lei-etal-2018-sequicity,
title = "{S}equicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures",
author = "Lei, Wenqiang and
Jin, Xisen and
Kan, Min-Yen and
Ren, Zhaochun and
He, Xiangnan and
Yin, Dawei",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1133",
doi = "10.18653/v1/P18-1133",
pages = "1437--1447",
abstract = "Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning. A key contribution is that we design text spans named belief spans to track dialogue believes, allowing task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: significantly reducing model complexity in terms of number of parameters and training time by a magnitude. It significantly outperforms state-of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.",
}
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<abstract>Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning. A key contribution is that we design text spans named belief spans to track dialogue believes, allowing task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: significantly reducing model complexity in terms of number of parameters and training time by a magnitude. It significantly outperforms state-of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.</abstract>
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%0 Conference Proceedings
%T Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures
%A Lei, Wenqiang
%A Jin, Xisen
%A Kan, Min-Yen
%A Ren, Zhaochun
%A He, Xiangnan
%A Yin, Dawei
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F lei-etal-2018-sequicity
%X Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised or reinforcement learning. A key contribution is that we design text spans named belief spans to track dialogue believes, allowing task-oriented dialogue systems to be modeled in a seq2seq way. Based on this, we propose a simplistic Two Stage CopyNet instantiation which emonstrates good scalability: significantly reducing model complexity in terms of number of parameters and training time by a magnitude. It significantly outperforms state-of-the-art pipeline-based methods on large datasets and retains a satisfactory entity match rate on out-of-vocabulary (OOV) cases where pipeline-designed competitors totally fail.
%R 10.18653/v1/P18-1133
%U https://aclanthology.org/P18-1133
%U https://doi.org/10.18653/v1/P18-1133
%P 1437-1447
Markdown (Informal)
[Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures](https://aclanthology.org/P18-1133) (Lei et al., ACL 2018)
ACL