@inproceedings{xu-etal-2024-learning,
title = "Learning to Match Representations is Better for End-to-End Task-Oriented Dialog System",
author = "Xu, Wanshi and
Cheng, Xuxin and
Zhu, Zhihong and
Chen, Zhanpeng and
Zou, Yuexian",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.609/",
doi = "10.18653/v1/2024.findings-emnlp.609",
pages = "10409--10419",
abstract = "Due to the rapid development with pre-trained language models, fully end-to-end Task-Oriented Dialogue (TOD) systems exhibit superior performance. How to achieve the ability to efficiently retrieve entities in cross-domain large-scale databases is a key issue. Most existing end-to-end Task-Oriented Dialogue systems suffer from the following problems: The ability to handle erroneous but easily confused entities needs to be improved; Matching information between contexts and entities is not captured, leading to weak modeling of domain-invariant and interpretable features, making it difficult to generalize to unseen domains. In this paper, we propose a method for knowledge retrieval driven by matching representations. The approach consists of a matching signal extractor for extracting matching representations between contexts and entities that have generic conceptual features and hence domain invariant properties, and an Attribute Filter for filtering irrelevant information to facilitate the re-selection of entities. Experiments on three standard benchmarks at the dialogue level and on large knowledge bases show that our retriever performs knowledge retrieval more efficiently than existing approaches."
}
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<abstract>Due to the rapid development with pre-trained language models, fully end-to-end Task-Oriented Dialogue (TOD) systems exhibit superior performance. How to achieve the ability to efficiently retrieve entities in cross-domain large-scale databases is a key issue. Most existing end-to-end Task-Oriented Dialogue systems suffer from the following problems: The ability to handle erroneous but easily confused entities needs to be improved; Matching information between contexts and entities is not captured, leading to weak modeling of domain-invariant and interpretable features, making it difficult to generalize to unseen domains. In this paper, we propose a method for knowledge retrieval driven by matching representations. The approach consists of a matching signal extractor for extracting matching representations between contexts and entities that have generic conceptual features and hence domain invariant properties, and an Attribute Filter for filtering irrelevant information to facilitate the re-selection of entities. Experiments on three standard benchmarks at the dialogue level and on large knowledge bases show that our retriever performs knowledge retrieval more efficiently than existing approaches.</abstract>
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%0 Conference Proceedings
%T Learning to Match Representations is Better for End-to-End Task-Oriented Dialog System
%A Xu, Wanshi
%A Cheng, Xuxin
%A Zhu, Zhihong
%A Chen, Zhanpeng
%A Zou, Yuexian
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F xu-etal-2024-learning
%X Due to the rapid development with pre-trained language models, fully end-to-end Task-Oriented Dialogue (TOD) systems exhibit superior performance. How to achieve the ability to efficiently retrieve entities in cross-domain large-scale databases is a key issue. Most existing end-to-end Task-Oriented Dialogue systems suffer from the following problems: The ability to handle erroneous but easily confused entities needs to be improved; Matching information between contexts and entities is not captured, leading to weak modeling of domain-invariant and interpretable features, making it difficult to generalize to unseen domains. In this paper, we propose a method for knowledge retrieval driven by matching representations. The approach consists of a matching signal extractor for extracting matching representations between contexts and entities that have generic conceptual features and hence domain invariant properties, and an Attribute Filter for filtering irrelevant information to facilitate the re-selection of entities. Experiments on three standard benchmarks at the dialogue level and on large knowledge bases show that our retriever performs knowledge retrieval more efficiently than existing approaches.
%R 10.18653/v1/2024.findings-emnlp.609
%U https://aclanthology.org/2024.findings-emnlp.609/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.609
%P 10409-10419
Markdown (Informal)
[Learning to Match Representations is Better for End-to-End Task-Oriented Dialog System](https://aclanthology.org/2024.findings-emnlp.609/) (Xu et al., Findings 2024)
ACL