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        <title>Towards Improved Distantly Supervised Multilingual Named-Entity Recognition for Tweets</title>
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    <abstract>Recent low-resource named-entity recognition (NER) work has shown impressive gains by leveraging a single multilingual model trained using distantly supervised data derived from cross-lingual knowledge bases. In this work, we investigate such approaches by leveraging Wikidata to build large-scale NER datasets of Tweets and propose two orthogonal improvements for low-resource NER in the Twitter social media domain: (1) leveraging domain-specific pre-training on Tweets; and (2) building a model for each language family rather than an all-in-one single multilingual model. For (1), we show that mBERT with Tweet pre-training outperforms the state-of-the-art multilingual transformer-based language model, LaBSE, by a relative increase of 34.6% in F1 when evaluated on Twitter data in a language-agnostic multilingual setting. For (2), we show that learning NER models for language families outperforms a single multilingual model by relative increases of 14.1%, 15.8% and 45.3% in F1 when utilizing mBERT, mBERT with Tweet pre-training and LaBSE, respectively. We conduct analyses and present examples for these observed improvements.</abstract>
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%0 Conference Proceedings
%T Towards Improved Distantly Supervised Multilingual Named-Entity Recognition for Tweets
%A Eskander, Ramy
%A Mishra, Shubhanshu
%A Mehta, Sneha
%A Samaniego, Sofia
%A Haghighi, Aria
%S Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F eskander-etal-2022-towards
%X Recent low-resource named-entity recognition (NER) work has shown impressive gains by leveraging a single multilingual model trained using distantly supervised data derived from cross-lingual knowledge bases. In this work, we investigate such approaches by leveraging Wikidata to build large-scale NER datasets of Tweets and propose two orthogonal improvements for low-resource NER in the Twitter social media domain: (1) leveraging domain-specific pre-training on Tweets; and (2) building a model for each language family rather than an all-in-one single multilingual model. For (1), we show that mBERT with Tweet pre-training outperforms the state-of-the-art multilingual transformer-based language model, LaBSE, by a relative increase of 34.6% in F1 when evaluated on Twitter data in a language-agnostic multilingual setting. For (2), we show that learning NER models for language families outperforms a single multilingual model by relative increases of 14.1%, 15.8% and 45.3% in F1 when utilizing mBERT, mBERT with Tweet pre-training and LaBSE, respectively. We conduct analyses and present examples for these observed improvements.
%U https://aclanthology.org/2022.mrl-1.12
%P 115-124

Markdown (Informal)

[Towards Improved Distantly Supervised Multilingual Named-Entity Recognition for Tweets](https://aclanthology.org/2022.mrl-1.12) (Eskander et al., MRL 2022)

• Towards Improved Distantly Supervised Multilingual Named-Entity Recognition for Tweets (Eskander et al., MRL 2022)

ACL

• Ramy Eskander, Shubhanshu Mishra, Sneha Mehta, Sofia Samaniego, and Aria Haghighi. 2022. Towards Improved Distantly Supervised Multilingual Named-Entity Recognition for Tweets. In Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 115–124, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.