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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification

February 1, 2023

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Authors

Mozhi Zhang

University of Maryland


Yoshinari Fujinuma

University of Colorado


Jordan Boyd-Graber

University of Maryland


DOI:

10.1609/aaai.v34i05.6500


Abstract:

Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigate whether character-level knowledge transfer from a related language helps text classification. We present a cross-lingual document classification framework (caco) that exploits cross-lingual subword similarity by jointly training a character-based embedder and a word-based classifier. The embedder derives vector representations for input words from their written forms, and the classifier makes predictions based on the word vectors. We use a joint character representation for both the source language and the target language, which allows the embedder to generalize knowledge about source language words to target language words with similar forms. We propose a multi-task objective that can further improve the model if additional cross-lingual or monolingual resources are available. Experiments confirm that character-level knowledge transfer is more data-efficient than word-level transfer between related languages.

Topics: AAAI

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Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification Proceedings of the AAAI Conference on Artificial Intelligence (2020) 9547-9554.

Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification AAAI 2020, 9547-9554.

Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber (2020). Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 9547-9554.

Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.9547-9554.

Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber. 2020. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. "Proceedings of the AAAI Conference on Artificial Intelligence". 9547-9554.

Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber. (2020) "Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification", Proceedings of the AAAI Conference on Artificial Intelligence, p.9547-9554

Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber, "Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification", AAAI, p.9547-9554, 2020.

Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber. "Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.9547-9554.

Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber. "Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 9547-9554.

Mozhi Zhang||Yoshinari Fujinuma||Jordan Boyd-Graber. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. AAAI[Internet]. 2020[cited 2023]; 9547-9554.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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