Abstract
Understanding causal relationship is an importance part of natural language processing. We address the causal information extraction problem with different neural models built on top of pre-trained transformer-based language models for identifying Cause, Effect and Signal spans, from news data sets. We use the Causal News Corpus subtask 2 training data set to train span-based and sequence tagging models. Our span-based model based on pre-trained BERT base weights achieves an F1 score of 47.48 on the test set with an accuracy score of 36.87 and obtained 3rd place in the Causal News Corpus 2022 shared task.
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CITATION STYLE
Saha, A., Gittens, A., Yener, B., Hassanzadeh, O., Ni, J., & Srinivas, K. (2022). SPOCK @ Causal News Corpus 2022: Cause-Effect-Signal Span Detection Using Span-Based and Sequence Tagging Models. In CASE 2022 - 5th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, Proceedings of the Workshop (pp. 133–137). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.case-1.18
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