@inproceedings{madotto-etal-2020-learning,
title = "Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems",
author = "Madotto, Andrea and
Cahyawijaya, Samuel and
Winata, Genta Indra and
Xu, Yan and
Liu, Zihan and
Lin, Zhaojiang and
Fung, Pascale",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.215",
doi = "10.18653/v1/2020.findings-emnlp.215",
pages = "2372--2394",
abstract = "Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (KB) plays an essential role in fulfilling user requests. Modularized systems rely on DST to interact with the KB, which is expensive in terms of annotation and inference time. End-to-end systems, instead, use the KB directly as input, but they cannot scale when the KB is larger than a few hundred entries. In this paper, we propose a method to embed the KB, of any size, directly into the model parameters. The resulting model does not require any DST or template responses, nor the KB as input, and it can dynamically update its KB via fine-tuning. We evaluate our solution in five task-oriented dialogue datasets with small, medium, and large KB size. Our experiments show that end-to-end models can effectively embed knowledge bases in their parameters and achieve competitive performance in all evaluated datasets.",
}
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<abstract>Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (KB) plays an essential role in fulfilling user requests. Modularized systems rely on DST to interact with the KB, which is expensive in terms of annotation and inference time. End-to-end systems, instead, use the KB directly as input, but they cannot scale when the KB is larger than a few hundred entries. In this paper, we propose a method to embed the KB, of any size, directly into the model parameters. The resulting model does not require any DST or template responses, nor the KB as input, and it can dynamically update its KB via fine-tuning. We evaluate our solution in five task-oriented dialogue datasets with small, medium, and large KB size. Our experiments show that end-to-end models can effectively embed knowledge bases in their parameters and achieve competitive performance in all evaluated datasets.</abstract>
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%0 Conference Proceedings
%T Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems
%A Madotto, Andrea
%A Cahyawijaya, Samuel
%A Winata, Genta Indra
%A Xu, Yan
%A Liu, Zihan
%A Lin, Zhaojiang
%A Fung, Pascale
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F madotto-etal-2020-learning
%X Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (KB) plays an essential role in fulfilling user requests. Modularized systems rely on DST to interact with the KB, which is expensive in terms of annotation and inference time. End-to-end systems, instead, use the KB directly as input, but they cannot scale when the KB is larger than a few hundred entries. In this paper, we propose a method to embed the KB, of any size, directly into the model parameters. The resulting model does not require any DST or template responses, nor the KB as input, and it can dynamically update its KB via fine-tuning. We evaluate our solution in five task-oriented dialogue datasets with small, medium, and large KB size. Our experiments show that end-to-end models can effectively embed knowledge bases in their parameters and achieve competitive performance in all evaluated datasets.
%R 10.18653/v1/2020.findings-emnlp.215
%U https://aclanthology.org/2020.findings-emnlp.215
%U https://doi.org/10.18653/v1/2020.findings-emnlp.215
%P 2372-2394
Markdown (Informal)
[Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems](https://aclanthology.org/2020.findings-emnlp.215) (Madotto et al., Findings 2020)
ACL