@inproceedings{eskander-etal-2022-towards,
    title = "Towards Improved Distantly Supervised Multilingual Named-Entity Recognition for Tweets",
    author = "Eskander, Ramy  and
      Mishra, Shubhanshu  and
      Mehta, Sneha  and
      Samaniego, Sofia  and
      Haghighi, Aria",
    booktitle = "Proceedings of the The 2nd Workshop on Multi-lingual Representation Learning (MRL)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.mrl-1.12",
    pages = "115--124",
    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.",
}