ACL 2024findings4 citations
Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models
Hiroshi Kanayama, Yang Zhao, Ran Iwamoto, Takuya Ohko
Abstract
This paper exploits a sentiment extractor supported by syntactic and lexical resources to enhance multilingual sentiment classification solved through the generative approach, without retraining LLMs. By adding external information of words and phrases that have positive/negative polarities, the multilingual sentiment classification error was reduced by up to 33 points, and the combination of two approaches performed best especially in high-performing pairs of LLMs and languages.
BibTeX
@inproceedings{kanayama-etal-2024-incorporating,
title = "Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models",
author = "Kanayama, Hiroshi and
Zhao, Yang and
Iwamoto, Ran and
Ohko, Takuya",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.286/",
doi = "10.18653/v1/2024.findings-acl.286",
pages = "4810--4817"
}