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        <title>Optimizing Entity Resolution in Voice Interfaces: An ASR-Aware Entity Reference Expansion Approach</title>
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    <abstract>This paper tackles the challenges presented by Automatic Speech Recognition (ASR) errors in voice-based dialog systems, specifically, their adverse impact on Entity Resolution (ER) as a downstream task. Navigating the equilibrium between accuracy and online retrieval’s speed requirement proves challenging, particularly when limited data links the failed mentions to resolved entities. In this paper, we propose a entity reference expansion system, injecting pairs of failed mentions and resolved entity names into the knowledge graph, enhancing its awareness of unresolved mentions. To address data scarcity, we introduce a synthetic data generation approach aligned with noise patterns. This, combined with an ASR-Error-Aware Loss function, facilitates the training of a RoBERTa model, which filters failed mentions and extracts entity pairs for knowledge graph expansion. These designs confront obstacles related to ASR noise, data limitations, and online entity retrieval.</abstract>
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%0 Conference Proceedings
%T Optimizing Entity Resolution in Voice Interfaces: An ASR-Aware Entity Reference Expansion Approach
%A Chen, Jiangning
%A Zhang, Ziyun
%A Hu, Qianli
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F chen-etal-2024-optimizing
%X This paper tackles the challenges presented by Automatic Speech Recognition (ASR) errors in voice-based dialog systems, specifically, their adverse impact on Entity Resolution (ER) as a downstream task. Navigating the equilibrium between accuracy and online retrieval’s speed requirement proves challenging, particularly when limited data links the failed mentions to resolved entities. In this paper, we propose a entity reference expansion system, injecting pairs of failed mentions and resolved entity names into the knowledge graph, enhancing its awareness of unresolved mentions. To address data scarcity, we introduce a synthetic data generation approach aligned with noise patterns. This, combined with an ASR-Error-Aware Loss function, facilitates the training of a RoBERTa model, which filters failed mentions and extracts entity pairs for knowledge graph expansion. These designs confront obstacles related to ASR noise, data limitations, and online entity retrieval.
%U https://aclanthology.org/2024.emnlp-industry.1
%P 1-7

Markdown (Informal)

[Optimizing Entity Resolution in Voice Interfaces: An ASR-Aware Entity Reference Expansion Approach](https://aclanthology.org/2024.emnlp-industry.1) (Chen et al., EMNLP 2024)

• Optimizing Entity Resolution in Voice Interfaces: An ASR-Aware Entity Reference Expansion Approach (Chen et al., EMNLP 2024)

ACL

• Jiangning Chen, Ziyun Zhang, and Qianli Hu. 2024. Optimizing Entity Resolution in Voice Interfaces: An ASR-Aware Entity Reference Expansion Approach. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1–7, Miami, Florida, US. Association for Computational Linguistics.