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        <title>Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference</title>
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    <name type="personal">
        <namePart type="given">Jing</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>Named entity recognition is a key component of many text processing pipelines and it is thus essential for this component to be robust to different types of input. However, domain transfer of NER models with data from multiple genres has not been widely studied. To this end, we conduct NER experiments in three predictive setups on data from: a) multiple domains; b) multiple domains where the genre label is unknown at inference time; c) domains not encountered in training. We introduce a new architecture tailored to this task by using shared and private domain parameters and multi-task learning. This consistently outperforms all other baseline and competitive methods on all three experimental setups, with differences ranging between +1.95 to +3.11 average F1 across multiple genres when compared to standard approaches. These results illustrate the challenges that need to be taken into account when building real-world NLP applications that are robust to various types of text and the methods that can help, at least partially, alleviate these issues.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.750</identifier>
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%0 Conference Proceedings
%T Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference
%A Wang, Jing
%A Kulkarni, Mayank
%A Preotiuc-Pietro, Daniel
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-multi-domain-named
%X Named entity recognition is a key component of many text processing pipelines and it is thus essential for this component to be robust to different types of input. However, domain transfer of NER models with data from multiple genres has not been widely studied. To this end, we conduct NER experiments in three predictive setups on data from: a) multiple domains; b) multiple domains where the genre label is unknown at inference time; c) domains not encountered in training. We introduce a new architecture tailored to this task by using shared and private domain parameters and multi-task learning. This consistently outperforms all other baseline and competitive methods on all three experimental setups, with differences ranging between +1.95 to +3.11 average F1 across multiple genres when compared to standard approaches. These results illustrate the challenges that need to be taken into account when building real-world NLP applications that are robust to various types of text and the methods that can help, at least partially, alleviate these issues.
%R 10.18653/v1/2020.acl-main.750
%U https://aclanthology.org/2020.acl-main.750
%U https://doi.org/10.18653/v1/2020.acl-main.750
%P 8476-8488

Markdown (Informal)

[Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference](https://aclanthology.org/2020.acl-main.750) (Wang et al., ACL 2020)

• Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference (Wang et al., ACL 2020)

ACL

• Jing Wang, Mayank Kulkarni, and Daniel Preotiuc-Pietro. 2020. Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8476–8488, Online. Association for Computational Linguistics.