Abstract
This is an overview paper of the NLPCC 2021 shared task on AutoIE2, which aims to evaluate the sub-event identification systems with limited annotated data. Given definitions of specific sub-events, 100K unannotated samples and 300 annotated seed samples, participants are required to build a sub-event identification system. 30 teams registered and 14 of them submitted results. The top system achieves \(8.43\%\) and \(8.25\%\) accuracy score improvement upon the baseline system with or without extra annotated data respectively. The evaluation result indicates that it is possible to use less human annotation and large unlabeled corpora for the sub-event identification system. ALL information about this task can be found at https://github.com/IIGROUP/AutoIE2.
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Guo, W. et al. (2021). Overview of the NLPCC 2021 Shared Task: AutoIE2. In: Wang, L., Feng, Y., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2021. Lecture Notes in Computer Science(), vol 13029. Springer, Cham. https://doi.org/10.1007/978-3-030-88483-3_42
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