CL-XABSA: Contrastive Learning for Cross-Lingual Aspect-Based Sentiment Analysis-Reference-Cited by-同舟云学术

CL-XABSA: Contrastive Learning for Cross-Lingual Aspect-Based Sentiment Analysis

Published:2023 Issue: Volume:31 Page:2935-2946
ISSN:2329-9290
Container-title:IEEE/ACM Transactions on Audio, Speech, and Language Processing
language:
Short-container-title:IEEE/ACM Trans. Audio Speech Lang. Process.

Author:

Lin Nankai¹^ORCID,Fu Yingwen²^ORCID,Lin Xiaotian²^ORCID,Zhou Dong²^ORCID,Yang Aimin¹^ORCID,Jiang Shengyi³^ORCID

Affiliation:

1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

2. School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China

3. Guangzhou Key Laboratory of Multilingual Intelligent Processing, School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China

Funder

Guangdong Basic and Applied Basic Research Foundation of China

Special Funds for the Cultivation of Guangdong College Student's Scientific and Technological Innovation Climbing Program Special Funds

Philosophy and Social Sciences 14th Five-Year Plan Project of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics

Link

http://xplorestaging.ieee.org/ielx7/6570655/9970249/10190176.pdf?arnumber=10190176

Reference40 articles.

1. Data-efficient image recognition with contrastive predictive coding;henaff;Proc Int Conf Mach Learn,2020

2. Transition-based Adversarial Network for Cross-lingual Aspect Extraction

3. An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis

4. A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models

5. Xtreme: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation;hu;Proc Int Conf Mach Learn,2020

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