Abstract
Temporal-relation classification plays an important role in the field of natural language processing. Various deep learning-based classifiers, which can generate better models using sentence embedding, have been proposed to address this challenging task. These approaches, however, do not work well because of the lack of task-related information. To overcome this problem, we propose a novel framework that incorporates prior information by employing awareness of events and time expressions (time–event entities) as a filter. We name this module “question encoder.” In our approach, this kind of prior information can extract task-related information from sentence embedding. Our experimental results on a publicly available Timebank-Dense corpus demonstrate that our approach outperforms some state-of-the-art techniques.
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Acknowledgements
This work was partially supported by a JSPS Grant-in-Aid for Scientific Research (B) (#19H04420).
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Seki, Y., Zhao, K., Oguni, M., Sugiyama, K. (2020). A Framework for Classifying Temporal Relations with Question Encoder. In: Ishita, E., Pang, N.L.S., Zhou, L. (eds) Digital Libraries at Times of Massive Societal Transition. ICADL 2020. Lecture Notes in Computer Science(), vol 12504. Springer, Cham. https://doi.org/10.1007/978-3-030-64452-9_2
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