RECESS: Resource for Extracting Cause, Effect, and Signal Spans
Published in 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, 2023
Fiona Anting Tan, Hansi Hettiarachchi, Ali Hürriyetoglu, Nelleke Oostdijk, Tommaso Caselli, Tadashi Nomoto, Onur Uca, Farhana Ferdousi Liza, and See-Kiong Ng. 2023. RECESS: Resource for extracting cause, effect, and signal spans. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, Bali, Indonesia. Association for Computational Linguistics. [forthcoming]
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Abstract
Causality expresses the relation between two arguments, one of which represents the cause and the other the effect (or consequence). Causal relations are fundamental to human decision making and reasoning, and extracting them from natural language texts is crucial for building effective natural language understanding models. However, the scarcity of annotated corpora for causal relations poses a challenge in the development of such tools. Thus, we created Resource for Extracting Cause, Effect, and Signal Spans (RECESS), a comprehensive corpus annotated for causality at different levels, including Cause, Effect, and Signal spans. The corpus contains 3,767 sentences, of which, 1,982 are causal sentences that contain a total of 2,754 causal relations. We report baseline experiments on two natural language tasks (Causal Sentence Classification, and Cause-Effect-Signal Span Detection), and establish initial benchmarks for future work. We conduct an in-depth analysis of the corpus and the properties of causal relations in text. RECESS is a valuable resource for developing and evaluating causal relation extraction models, benefiting researchers working on topics from information retrieval to natural language understanding and inference.