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

• URL: http://arxiv.org/abs/2110.07792v1
• Date: Fri, 15 Oct 2021 01:10:50 GMT
• Title: A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification
• Authors: Sosuke Nishikawa, Ikuya Yamada, Yoshimasa Tsuruoka, Isao Echizen
• Abstract summary: We present a multilingual bag-of-entities model that boosts the performance of zero-shot cross-lingual text classification. It leverages the multilingual nature of Wikidata: entities in multiple languages representing the same concept are defined with a unique identifier. A model trained on entity features in a resource-rich language can thus be directly applied to other languages.
• Score: 16.684856745734944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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