Bo Ma


ASCM: An Answer Space Clustered Prompting Method without Answer Engineering
Zhen Wang | Yating Yang | Zhou Xi | Bo Ma | Lei Wang | Rui Dong | Azmat Anwar
Findings of the Association for Computational Linguistics: ACL 2022

Prompt-based learning, which exploits knowledge from pre-trained language models by providing textual prompts and designing appropriate answer-category mapping methods, has achieved impressive successes on few-shot text classification and natural language inference (NLI). Because of the diverse linguistic expression, there exist many answer tokens for the same category. However, both manual answer design and automatic answer search constrain answer space and therefore hardly achieve ideal performance. To address this issue, we propose an answer space clustered prompting model (ASCM) together with a synonym initialization method (SI) which automatically categorizes all answer tokens in a semantic-clustered embedding space. We also propose a stable semi-supervised method named stair learning (SL) that orderly distills knowledge from better models to weaker models. Extensive experiments demonstrate that our ASCM+SL significantly outperforms existing state-of-the-art techniques in few-shot settings.


基于时间注意力胶囊网络的维吾尔语情感分类模型(Uyghur Sentiment Classification Model Based on Temporal Attention Capsule Networks)
Hantian Luo (罗涵天) | Yating Yang (杨雅婷) | Rui Dong (董瑞) | Bo Ma (马博)
Proceedings of the 20th Chinese National Conference on Computational Linguistics