Distillation Language Adversarial Network for Cross-lingual Sentiment Analysis

• Published in: 2022 International Conference on Asian Language Processing (IALP) pp. 45 - 50
• Main Authors: Wang, Deheng, Yang, Aimin, Zhou, Yongmei, Xie, Fenfang, Ouyang, Zhouhao, Peng, Sancheng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.10.2022
• Subjects: Adaptation models; Adversarial network; Analytical models; Cross-lingual sentiment analysis; Knowledge distillation; Pre-trained model; Predictive models; Sentiment analysis; Training; Training data; Visualization
• DOI: 10.1109/IALP57159.2022.9961285

Cross-lingual sentiment analysis aims at tackling the lack of annotated corpus of variant low-resource languages by training a common classifier, to transfer the knowledge learned from the source language to target languages. Existing large-scale pre-trained language models have got remarkable improvements in cross-lingual sentiment analysis. However, these models still suffer from lack of annotated corpus for low-resource languages. To address such problems, we propose an end-to-end sentiment analysis architecture for cross-lingual sentiment analysis, named Distillation Language Adversarial Network (DLAN). Based on pre-trained model, DLAN uses adversarial learning with knowledge distillation to learn language invariant features without extra training data. We evaluate the proposed method on Amazon review dataset, a multilingual sentiment dataset. The results illustrate that DLAN is more effective than the baseline methods in cross-lingual sentiment analysis.