Name
Abstract | ||
|---|---|---|
Existing methods usually identify causal relations between events at the mention-level, which takes each event mention pair as a separate input. As a result, they either suffer from conflicts among causal relations predicted separately or require a set of additional constraints to resolve such conflicts. We propose to study this task in a more realistic setting, where event-level causality identification can be made. The advantage is two folds: 1) with modeling different mentions of an event as a single unit, no more conflicts among predicted results, without any extra constraints; 2) with the use of diverse knowledge sources (e.g., co-occurrence and coreference relations), a rich graph-based event structure can be induced from the document for supporting event-level causal inference. Graph convolutional network is used to encode such structural information, which aims to capture the local and non-local dependencies among nodes. Results show that our model achieves the best performance under both mention- and event-level settings, outperforming a number of strong baselines by at least 2.8% on F1 score. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3477495.3531758 | SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | DocType | Citations |
Event causality identification, inconsistency, graph neural network | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuang Fan | 1 | 4 | 1.45 |
| Daoxing Liu | 2 | 0 | 0.34 |
| Libo Qin | 3 | 6 | 6.22 |
| Yue Zhang | 4 | 1364 | 114.17 |
| Xu Ruifeng | 5 | 432 | 53.04 |