What is the Essence of a Claim? Cross-Domain Claim Identification
This work addresses the challenge of inconsistent claim definitions in NLP argument mining, which is incremental in improving cross-domain applications.
The study analyzed six datasets to reveal differing conceptualizations of claims in argument mining, and through cross-domain experiments, found that shared lexical properties and system configurations can mitigate the negative impact on classification performance.
Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent perception of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.