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        <title>A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification</title>
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    <name type="personal">
        <namePart type="given">Sosuke</namePart>
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            <namePart type="given">Vivek</namePart>
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    <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>
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%0 Conference Proceedings
%T A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification
%A Nishikawa, Sosuke
%A Yamada, Ikuya
%A Tsuruoka, Yoshimasa
%A Echizen, Isao
%Y Fokkens, Antske
%Y Srikumar, Vivek
%S Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F nishikawa-etal-2022-multilingual
%X 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.
%R 10.18653/v1/2022.conll-1.1
%U https://aclanthology.org/2022.conll-1.1/
%U https://doi.org/10.18653/v1/2022.conll-1.1
%P 1-12

Markdown (Informal)

[A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification](https://aclanthology.org/2022.conll-1.1/) (Nishikawa et al., CoNLL 2022)

• A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification (Nishikawa et al., CoNLL 2022)

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.