A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification

Sosuke Nishikawa, Ikuya Yamada, Yoshimasa Tsuruoka, Isao Echizen


Abstract
We present a multilingual bag-of-entities model that effectively boosts the performance of zero-shot cross-lingual text classification by extending a multilingual pre-trained language model (e.g., M-BERT). It leverages the multilingual nature of Wikidata: entities in multiple languages representing the same concept are defined with a unique identifier. This enables entities described in multiple languages to be represented using shared embeddings. A model trained on entity features in a resource-rich language can thus be directly applied to other languages. Our experimental results on cross-lingual topic classification (using the MLDoc and TED-CLDC datasets) and entity typing (using the SHINRA2020-ML dataset) show that the proposed model consistently outperforms state-of-the-art models.
Anthology ID:
2022.conll-1.1
Volume:
Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Venue:
CoNLL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–12
Language:
URL:
https://aclanthology.org/2022.conll-1.1
DOI:
Bibkey:
Cite (ACL):
Sosuke Nishikawa, Ikuya Yamada, Yoshimasa Tsuruoka, and Isao Echizen. 2022. A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification. In Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL), pages 1–12, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification (Nishikawa et al., CoNLL 2022)
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PDF:
https://preview.aclanthology.org/author-url/2022.conll-1.1.pdf