TY - GEN
T1 - RECESS: Resource for Extracting Cause, Effect, and Signal Spans
AU - Tan, Fiona Anting
AU - Hettiarachchi, Hansi
AU - Hürriyetoğlu, Ali
AU - Oostdijk, Nelleke
AU - Caselli, Tommaso
AU - Nomoto, Tadashi
AU - Uca, Onur
AU - Liza, Farhana Ferdousi
AU - Ng, See Kiong
PY - 2023/11
Y1 - 2023/11
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105027223088
U2 - 10.18653/v1/2023.ijcnlp-main.6
DO - 10.18653/v1/2023.ijcnlp-main.6
M3 - Conference contribution
VL - 1: Long Papers
T3 - 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: Long Papers, IJCNLP-AACL 2023
SP - 66
EP - 82
BT - 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
A2 - Park, Jong C.
A2 - Arase, Yuki
A2 - Hu, Baotian
A2 - Lu, Wei
A2 - Wijaya, Derry
A2 - Purwarianti, Ayu
A2 - Krisnadhi, Adila Alfa
PB - Association for Computational Linguistics
ER -