Hierarchical Selection of Important Context for Generative Event Causality Identification with Optimal Transports

Hieu Man, Chien Van Nguyen, Nghia Trung Ngo, Linh Ngo, Franck Dernoncourt, Thien Huu Nguyen


Abstract
We study the problem of Event Causality Identification (ECI) that seeks to predict causal relation between event mentions in the text. In contrast to previous classification-based models, a few recent ECI methods have explored generative models to deliver state-of-the-art performance. However, such generative models cannot handle document-level ECI where long context between event mentions must be encoded to secure correct predictions. In addition, previous generative ECI methods tend to rely on external toolkits or human annotation to obtain necessary training signals. To address these limitations, we propose a novel generative framework that leverages Optimal Transport (OT) to automatically select the most important sentences and words from full documents. Specifically, we introduce hierarchical OT alignments between event pairs and the document to extract pertinent contexts. The selected sentences and words are provided as input and output to a T5 encoder-decoder model which is trained to generate both the causal relation label and salient contexts. This allows richer supervision without external tools. We conduct extensive evaluations on different datasets with multiple languages to demonstrate the benefits and state-of-the-art performance of ECI.
Anthology ID:
2024.lrec-main.711
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
8122–8132
Language:
URL:
https://aclanthology.org/2024.lrec-main.711
DOI:
Bibkey:
Cite (ACL):
Hieu Man, Chien Van Nguyen, Nghia Trung Ngo, Linh Ngo, Franck Dernoncourt, and Thien Huu Nguyen. 2024. Hierarchical Selection of Important Context for Generative Event Causality Identification with Optimal Transports. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8122–8132, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Hierarchical Selection of Important Context for Generative Event Causality Identification with Optimal Transports (Man et al., LREC-COLING 2024)
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PDF:
https://aclanthology.org/2024.lrec-main.711.pdf