Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929434534084608 |
|---|---|
| author | Lin, Jiayu Chen, Guanrong Jin, Bojun Li, Chenyang Jia, Shutong Lin, Wancong Sun, Yang He, Yuhang Yang, Caihua Bao, Jianzhu Wu, Jipeng Su, Wen Chen, Jinglu Li, Xinyi Chen, Tianyu Han, Mingjie Du, Shuaiwen Wang, Zijian Li, Jiyin Suo, Fuzhong Wang, Hao Lin, Nuanchen Huang, Xuanjing Jiang, Changjian Xu, RuiFeng Zhang, Long Cao, Jiuxin Jin, Ting Wei, Zhongyu |
| author_facet | Lin, Jiayu Chen, Guanrong Jin, Bojun Li, Chenyang Jia, Shutong Lin, Wancong Sun, Yang He, Yuhang Yang, Caihua Bao, Jianzhu Wu, Jipeng Su, Wen Chen, Jinglu Li, Xinyi Chen, Tianyu Han, Mingjie Du, Shuaiwen Wang, Zijian Li, Jiyin Suo, Fuzhong Wang, Hao Lin, Nuanchen Huang, Xuanjing Jiang, Changjian Xu, RuiFeng Zhang, Long Cao, Jiuxin Jin, Ting Wei, Zhongyu |
| contents | In this paper we present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023), and introduce the related datasets. We organize two tracks to handle the argumentative generation tasks in different scenarios, namely, Counter-Argument Generation (Track 1) and Claim-based Argument Generation (Track 2). Each track is equipped with its distinct dataset and baseline model respectively. In total, 32 competing teams register for the challenge, from which we received 11 successful submissions. In this paper, we will present the results of the challenge and a summary of the systems, highlighting commonalities and innovations among participating systems. Datasets and baseline models of the AI-Debater 2023 Challenge have been already released and can be accessed through the official website of the challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14829 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks Lin, Jiayu Chen, Guanrong Jin, Bojun Li, Chenyang Jia, Shutong Lin, Wancong Sun, Yang He, Yuhang Yang, Caihua Bao, Jianzhu Wu, Jipeng Su, Wen Chen, Jinglu Li, Xinyi Chen, Tianyu Han, Mingjie Du, Shuaiwen Wang, Zijian Li, Jiyin Suo, Fuzhong Wang, Hao Lin, Nuanchen Huang, Xuanjing Jiang, Changjian Xu, RuiFeng Zhang, Long Cao, Jiuxin Jin, Ting Wei, Zhongyu Computation and Language In this paper we present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023), and introduce the related datasets. We organize two tracks to handle the argumentative generation tasks in different scenarios, namely, Counter-Argument Generation (Track 1) and Claim-based Argument Generation (Track 2). Each track is equipped with its distinct dataset and baseline model respectively. In total, 32 competing teams register for the challenge, from which we received 11 successful submissions. In this paper, we will present the results of the challenge and a summary of the systems, highlighting commonalities and innovations among participating systems. Datasets and baseline models of the AI-Debater 2023 Challenge have been already released and can be accessed through the official website of the challenge. |
| title | Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.14829 |