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

2021-10-15 01:10:50
Sosuke Nishikawa, Ikuya Yamada, Yoshimasa Tsuruoka, Isao Echizen
arXiv_CL
               
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.

Abstract (translated)

URL

https://arxiv.org/abs/2110.07792

PDF

https://arxiv.org/pdf/2110.07792.pdf

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