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    <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.</abstract>
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%0 Conference Proceedings
%T Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models
%A Kanayama, Hiroshi
%A Zhao, Yang
%A Iwamoto, Ran
%A Ohko, Takuya
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F kanayama-etal-2024-incorporating
%X 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.
%U https://aclanthology.org/2024.findings-acl.286
%P 4810-4817

Markdown (Informal)

[Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models](https://aclanthology.org/2024.findings-acl.286) (Kanayama et al., Findings 2024)

• Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models (Kanayama et al., Findings 2024)

ACL

• Hiroshi Kanayama, Yang Zhao, Ran Iwamoto, and Takuya Ohko. 2024. Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 4810–4817, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.