Modern Talking in Key Point Analysis: Key Point Matching using Pretrained Encoders

Jan Heinrich Reimer, Thi Kim Hanh Luu, Max Henze, Yamen Ajjour


Abstract
We contribute to the ArgMining 2021 shared task on Quantitative Summarization and Key Point Analysis with two approaches for argument key point matching. For key point matching the task is to decide if a short key point matches the content of an argument with the same topic and stance towards the topic. We approach this task in two ways: First, we develop a simple rule-based baseline matcher by computing token overlap after removing stop words, stemming, and adding synonyms/antonyms. Second, we fine-tune pretrained BERT and RoBERTalanguage models as aregression classifier for only a single epoch. We manually examine errors of our proposed matcher models and find that long arguments are harder to classify. Our fine-tuned RoBERTa-Base model achieves a mean average precision score of 0.913, the best score for strict labels of all participating teams.
Anthology ID:
2021.argmining-1.18
Volume:
Proceedings of the 8th Workshop on Argument Mining
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
ArgMining | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
175–183
Language:
URL:
https://aclanthology.org/2021.argmining-1.18
DOI:
10.18653/v1/2021.argmining-1.18
Bibkey:
Cite (ACL):
Jan Heinrich Reimer, Thi Kim Hanh Luu, Max Henze, and Yamen Ajjour. 2021. Modern Talking in Key Point Analysis: Key Point Matching using Pretrained Encoders. In Proceedings of the 8th Workshop on Argument Mining, pages 175–183, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Modern Talking in Key Point Analysis: Key Point Matching using Pretrained Encoders (Reimer et al., ArgMining 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.argmining-1.18.pdf
Software:
 2021.argmining-1.18.Software.zip
Code
 heinrichreimer/modern-talking