Towards Improved Distantly Supervised Multilingual Named-Entity Recognition for Tweets
Ramy Eskander, Shubhanshu Mishra, Sneha Mehta, Sofia Samaniego, Aria Haghighi
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.- Anthology ID:
- 2022.mrl-1.12
- Volume:
- Proceedings of the 2nd Workshop on Multi-lingual Representation Learning (MRL)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Duygu Ataman, Hila Gonen, Sebastian Ruder, Orhan Firat, Gözde Gül Sahin, Jamshidbek Mirzakhalov
- Venue:
- MRL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 115–124
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.mrl-1.12/
- DOI:
- 10.18653/v1/2022.mrl-1.12
- Cite (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 2nd Workshop on Multi-lingual Representation Learning (MRL), pages 115–124, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Towards Improved Distantly Supervised Multilingual Named-Entity Recognition for Tweets (Eskander et al., MRL 2022)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.mrl-1.12.pdf