@inproceedings{kang-etal-2024-tmak,
title = "{TMAK}-Plus at {SIGHAN}-2024 dim{ABSA} Task: Multi-Agent Collaboration for Transparent and Rational Sentiment Analysis",
author = "Kang, Xin and
Zhang, Zhifei and
周嘉政, 周嘉政 and
Raino.wu@dataarobotics.com, Raino.wu@dataarobotics.com and
2020010107@mail.hfut.edu.cn, 2020010107@mail.hfut.edu.cn and
Matsumoto, Kazuyuki",
editor = "Wong, Kam-Fai and
Zhang, Min and
Xu, Ruifeng and
Li, Jing and
Wei, Zhongyu and
Gui, Lin and
Liang, Bin and
Zhao, Runcong",
booktitle = "Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sighan-1.10",
pages = "88--95",
abstract = "The TMAK-Plus team proposes a Multi-Agent Collaboration (MAC) model for the dimensional Aspect-Based Sentiment Analysis (dimABSA) task at SIGHAN-2024. The MAC model leverages Neuro-Symbolic AI to solve dimABSA transparently and rationally through symbolic message exchanges among generative AI agents. These agents collaborate on aspect detection, opinion detection, aspect classification, and intensity estimation. We created 8 sentiment intensity agents with distinct character traits to mimic diverse sentiment perceptions and average their outputs. The AI agents received clear instructions and 20 training examples to ensure task understanding. Our results suggest that the MAC model is effective in solving the dimABSA task and offers a transparent and rational approach to understanding the solution process.",
}
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<abstract>The TMAK-Plus team proposes a Multi-Agent Collaboration (MAC) model for the dimensional Aspect-Based Sentiment Analysis (dimABSA) task at SIGHAN-2024. The MAC model leverages Neuro-Symbolic AI to solve dimABSA transparently and rationally through symbolic message exchanges among generative AI agents. These agents collaborate on aspect detection, opinion detection, aspect classification, and intensity estimation. We created 8 sentiment intensity agents with distinct character traits to mimic diverse sentiment perceptions and average their outputs. The AI agents received clear instructions and 20 training examples to ensure task understanding. Our results suggest that the MAC model is effective in solving the dimABSA task and offers a transparent and rational approach to understanding the solution process.</abstract>
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%0 Conference Proceedings
%T TMAK-Plus at SIGHAN-2024 dimABSA Task: Multi-Agent Collaboration for Transparent and Rational Sentiment Analysis
%A Kang, Xin
%A Zhang, Zhifei
%A 周嘉政, 周嘉政
%A Raino.wu@dataarobotics.com, Raino.wu@dataarobotics.com
%A 2020010107@mail.hfut.edu.cn, 2020010107@mail.hfut.edu.cn
%A Matsumoto, Kazuyuki
%Y Wong, Kam-Fai
%Y Zhang, Min
%Y Xu, Ruifeng
%Y Li, Jing
%Y Wei, Zhongyu
%Y Gui, Lin
%Y Liang, Bin
%Y Zhao, Runcong
%S Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F kang-etal-2024-tmak
%X The TMAK-Plus team proposes a Multi-Agent Collaboration (MAC) model for the dimensional Aspect-Based Sentiment Analysis (dimABSA) task at SIGHAN-2024. The MAC model leverages Neuro-Symbolic AI to solve dimABSA transparently and rationally through symbolic message exchanges among generative AI agents. These agents collaborate on aspect detection, opinion detection, aspect classification, and intensity estimation. We created 8 sentiment intensity agents with distinct character traits to mimic diverse sentiment perceptions and average their outputs. The AI agents received clear instructions and 20 training examples to ensure task understanding. Our results suggest that the MAC model is effective in solving the dimABSA task and offers a transparent and rational approach to understanding the solution process.
%U https://aclanthology.org/2024.sighan-1.10
%P 88-95
Markdown (Informal)
[TMAK-Plus at SIGHAN-2024 dimABSA Task: Multi-Agent Collaboration for Transparent and Rational Sentiment Analysis](https://aclanthology.org/2024.sighan-1.10) (Kang et al., SIGHAN-WS 2024)
ACL