@inproceedings{shang-etal-2021-entity,
title = "Entity Resolution in Open-domain Conversations",
author = "Shang, Mingyue and
Wang, Tong and
Eric, Mihail and
Chen, Jiangning and
Wang, Jiyang and
Welch, Matthew and
Deng, Tiantong and
Grewal, Akshay and
Wang, Han and
Liu, Yue and
Liu, Yang and
Hakkani-Tur, Dilek",
editor = "Kim, Young-bum and
Li, Yunyao and
Rambow, Owen",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-industry.4",
doi = "10.18653/v1/2021.naacl-industry.4",
pages = "26--33",
abstract = "In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest. To improve the relevancy of retrieved knowledge, we propose a neural entity linking (NEL) approach. Different from formal documents, such as news, conversational utterances are informal and multi-turn, which makes it more challenging to disambiguate the entities. Therefore, we present a context-aware named entity recognition model (NER) and entity resolution (ER) model to utilize dialogue context information. We conduct NEL experiments on three open-domain conversation datasets and validate that incorporating context information improves the performance of NER and ER models. The end-to-end NEL approach outperforms the baseline by 62.8{\%} relatively in F1 metric. Furthermore, we verify that using external knowledge based on NEL benefits the neural response generation model.",
}
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<abstract>In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest. To improve the relevancy of retrieved knowledge, we propose a neural entity linking (NEL) approach. Different from formal documents, such as news, conversational utterances are informal and multi-turn, which makes it more challenging to disambiguate the entities. Therefore, we present a context-aware named entity recognition model (NER) and entity resolution (ER) model to utilize dialogue context information. We conduct NEL experiments on three open-domain conversation datasets and validate that incorporating context information improves the performance of NER and ER models. The end-to-end NEL approach outperforms the baseline by 62.8% relatively in F1 metric. Furthermore, we verify that using external knowledge based on NEL benefits the neural response generation model.</abstract>
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%0 Conference Proceedings
%T Entity Resolution in Open-domain Conversations
%A Shang, Mingyue
%A Wang, Tong
%A Eric, Mihail
%A Chen, Jiangning
%A Wang, Jiyang
%A Welch, Matthew
%A Deng, Tiantong
%A Grewal, Akshay
%A Wang, Han
%A Liu, Yue
%A Liu, Yang
%A Hakkani-Tur, Dilek
%Y Kim, Young-bum
%Y Li, Yunyao
%Y Rambow, Owen
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F shang-etal-2021-entity
%X In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest. To improve the relevancy of retrieved knowledge, we propose a neural entity linking (NEL) approach. Different from formal documents, such as news, conversational utterances are informal and multi-turn, which makes it more challenging to disambiguate the entities. Therefore, we present a context-aware named entity recognition model (NER) and entity resolution (ER) model to utilize dialogue context information. We conduct NEL experiments on three open-domain conversation datasets and validate that incorporating context information improves the performance of NER and ER models. The end-to-end NEL approach outperforms the baseline by 62.8% relatively in F1 metric. Furthermore, we verify that using external knowledge based on NEL benefits the neural response generation model.
%R 10.18653/v1/2021.naacl-industry.4
%U https://aclanthology.org/2021.naacl-industry.4
%U https://doi.org/10.18653/v1/2021.naacl-industry.4
%P 26-33
Markdown (Informal)
[Entity Resolution in Open-domain Conversations](https://aclanthology.org/2021.naacl-industry.4) (Shang et al., NAACL 2021)
ACL
- Mingyue Shang, Tong Wang, Mihail Eric, Jiangning Chen, Jiyang Wang, Matthew Welch, Tiantong Deng, Akshay Grewal, Han Wang, Yue Liu, Yang Liu, and Dilek Hakkani-Tur. 2021. Entity Resolution in Open-domain Conversations. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 26–33, Online. Association for Computational Linguistics.