%0 Conference Proceedings %T On Selecting Training Corpora for Cross-Domain Claim Detection %A Schaefer, Robin %A Knaebel, René %A Stede, Manfred %Y Lapesa, Gabriella %Y Schneider, Jodi %Y Jo, Yohan %Y Saha, Sougata %S Proceedings of the 9th Workshop on Argument Mining %D 2022 %8 October %I International Conference on Computational Linguistics %C Online and in Gyeongju, Republic of Korea %F schaefer-etal-2022-selecting %X Identifying claims in text is a crucial first step in argument mining. In this paper, we investigate factors for the composition of training corpora to improve cross-domain claim detection. To this end, we use four recent argumentation corpora annotated with claims and submit them to several experimental scenarios. Our results indicate that the “ideal” composition of training corpora is characterized by a large corpus size, homogeneous claim proportions, and less formal text domains. %U https://aclanthology.org/2022.argmining-1.17 %P 181-186