Lexicon-based fine-tuning of multilingual language models for low-resource language sentiment analysis

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Vinura Dhananjaya, Surangika Ranathunga, Sanath Jayasena
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引用次数: 0

Abstract

Pre-trained multilingual language models (PMLMs) such as mBERT and XLM-R have shown good cross-lingual transferability. However, they are not specifically trained to capture cross-lingual signals concerning sentiment words. This poses a disadvantage for low-resource languages (LRLs) that are under-represented in these models. To better fine-tune these models for sentiment classification in LRLs, a novel intermediate task fine-tuning (ITFT) technique based on a sentiment lexicon of a high-resource language (HRL) is introduced. The authors experiment with LRLs Sinhala, Tamil and Bengali for a 3-class sentiment classification task and show that this method outperforms vanilla fine-tuning of the PMLM. It also outperforms or is on-par with basic ITFT that relies on an HRL sentiment classification dataset.

Abstract Image

基于词典的多语种语言模型微调,用于低资源语言情感分析
预先训练的多语言语言模型(PMLM),如 mBERT 和 XLM-R,已经显示出良好的跨语言可移植性。然而,这些模型并没有经过专门训练,无法捕捉有关情感词的跨语言信号。这给低资源语言(LRL)带来了不利因素,因为这些语言在这些模型中的代表性不足。为了更好地针对 LRLs 中的情感分类对这些模型进行微调,介绍了一种基于高资源语言(HRL)情感词典的新型中间任务微调(ITFT)技术。作者用僧伽罗语、泰米尔语和孟加拉语的 LRL 进行了三类情感分类任务的实验,结果表明这种方法优于 PMLM 的香草微调。它还优于依赖于 HRL 情感分类数据集的基本 ITFT,或者与之相当。
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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