@inproceedings{muralidharan-etal-2021-noise,
title = "Noise Robust Named Entity Understanding for Voice Assistants",
author = "Muralidharan, Deepak and
Moniz, Joel Ruben Antony and
Gao, Sida and
Yang, Xiao and
Kao, Justine and
Pulman, Stephen and
Kothari, Atish and
Shen, Ray and
Pan, Yinying and
Kaul, Vivek and
Seyed Ibrahim, Mubarak and
Xiang, Gang and
Dun, Nan and
Zhou, Yidan and
O, Andy and
Zhang, Yuan and
Chitkara, Pooja and
Wang, Xuan and
Patel, Alkesh and
Tayal, Kushal and
Zheng, Roger and
Grasch, Peter and
Williams, Jason D and
Li, Lin",
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.25",
doi = "10.18653/v1/2021.naacl-industry.25",
pages = "196--204",
abstract = "Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13{\%} and EL accuracy by up to 3.6{\%} in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.",
}
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<abstract>Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.</abstract>
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%0 Conference Proceedings
%T Noise Robust Named Entity Understanding for Voice Assistants
%A Muralidharan, Deepak
%A Moniz, Joel Ruben Antony
%A Gao, Sida
%A Yang, Xiao
%A Kao, Justine
%A Pulman, Stephen
%A Kothari, Atish
%A Shen, Ray
%A Pan, Yinying
%A Kaul, Vivek
%A Seyed Ibrahim, Mubarak
%A Xiang, Gang
%A Dun, Nan
%A Zhou, Yidan
%A O, Andy
%A Zhang, Yuan
%A Chitkara, Pooja
%A Wang, Xuan
%A Patel, Alkesh
%A Tayal, Kushal
%A Zheng, Roger
%A Grasch, Peter
%A Williams, Jason D.
%A Li, Lin
%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 muralidharan-etal-2021-noise
%X Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.
%R 10.18653/v1/2021.naacl-industry.25
%U https://aclanthology.org/2021.naacl-industry.25
%U https://doi.org/10.18653/v1/2021.naacl-industry.25
%P 196-204
Markdown (Informal)
[Noise Robust Named Entity Understanding for Voice Assistants](https://aclanthology.org/2021.naacl-industry.25) (Muralidharan et al., NAACL 2021)
ACL
- Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, et al.. 2021. Noise Robust Named Entity Understanding for Voice Assistants. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 196–204, Online. Association for Computational Linguistics.