Only One Relation Possible? Modeling the Ambiguity in Event Temporal Relation Extraction
<|reference_start|>Only One Relation Possible? Modeling the Ambiguity in Event Temporal Relation Extraction: Event Temporal Relation Extraction (ETRE) aims to identify the temporal relationship between two events, which plays an important role in natural language understanding. Most previous works follow a single-label classification style, classifying an event pair into either a specific temporal relation (e.g., \textit{Before}, \textit{After}), or a special label \textit{Vague} when there may be multiple possible temporal relations between the pair. In our work, instead of directly making predictions on \textit{Vague}, we propose a multi-label classification solution for ETRE (METRE) to infer the possibility of each temporal relation independently, where we treat \textit{Vague} as the cases when there is more than one possible relation between two events. We design a speculation mechanism to explore the possible relations hidden behind \textit{Vague}, which enables the latent information to be used efficiently. Experiments on TB-Dense, MATRES and UDS-T show that our method can effectively utilize the \textit{Vague} instances to improve the recognition for specific temporal relations and outperforms most state-of-the-art methods.<|reference_end|>
@article{hu2024only,
    title={Only One Relation Possible? Modeling the Ambiguity in Event Temporal
  Relation Extraction},
    author={Yutong Hu and Quzhe Huang and Yansong Feng},
    journal={arXiv preprint arXiv:2408.07353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07353},
    primaryClass={cs.CL}
}