IMHO Fine-Tuning Improves Claim Detection
This work addresses claim detection for argumentation analysis in domains like social media and student essays, representing an incremental advance with a specific performance gain.
The paper tackled the problem of detecting claims across diverse domains by fine-tuning a language model on a novel Reddit corpus of 5.5 million self-labeled opinionated claims, resulting in an average improvement of 4 absolute F1 points in state-of-the-art performance across four benchmark datasets.
Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-labeled by their authors using the internet acronyms IMO/IMHO (in my (humble) opinion). Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. As these data sets include diverse domains such as social media and student essays this improvement demonstrates the robustness of fine-tuning on this novel corpus.