Exploring zero-shot and joint training cross-lingual strategies for aspect-based sentiment analysis based on contextualized multilingual language models-Reference-Cited by-同舟云学术

Exploring zero-shot and joint training cross-lingual strategies for aspect-based sentiment analysis based on contextualized multilingual language models

Published:2023-02-16 Issue:2 Volume:7 Page:121-143
ISSN:2475-1839
Container-title:Journal of Information and Telecommunication
language:en
Short-container-title:Journal of Information and Telecommunication

Author:

Van Thin Dang¹²^ORCID,Quoc Ngo Hung³,Ngoc Hao Duong¹²,Luu-Thuy Nguyen Ngan¹²

Affiliation:

1. Multimedia Communications Laboratory, University of Information Technology, Ho Chi Minh city, Vietnam

2. Vietnam National University, Ho Chi Minh city, Vietnam

3. School of Business Technology, Retail, and Supply Chain, Technological University Dublin, Dublin, Ireland

Funder

University of Information Technology-Vietnam National University

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications,Computer Science (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/24751839.2023.2173843

Reference58 articles.

1. Aharoni, R., Johnson, M. & Firat, O. (2019). Massively multilingual neural machine translation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 3874–3884). Minneapolis, Minnesota: Association for Computational Linguistics.

2. Tuning Multilingual Transformers for Language-Specific Named Entity Recognition

3. On Achieving and Evaluating Language-Independence in NLP

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