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http://dx.doi.org/10.6109/jkiice.2022.26.5.662

Semantic Role Labeling using Biaffine Average Attention Model  

Nam, Chung-Hyeon (Department of Computer Engineering, Korea University of Technology and Education)
Jang, Kyung-Sik (Department of Computer Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.5, 2022 , pp. 662-667 More about this Journal
Abstract
Semantic role labeling task(SRL) is to extract predicate and arguments such as agent, patient, place, time. In the previously SRL task studies, a pipeline method extracting linguistic features of sentence has been proposed, but in this method, errors of each extraction work in the pipeline affect semantic role labeling performance. Therefore, methods using End-to-End neural network model have recently been proposed. In this paper, we propose a neural network model using the Biaffine Average Attention model for SRL task. The proposed model consists of a structure that can focus on the entire sentence information regardless of the distance between the predicate in the sentence and the arguments, instead of LSTM model that uses the surrounding information for prediction of a specific token proposed in the previous studies. For evaluation, we used F1 scores to compare two models based BERT model that proposed in existing studies using F1 scores, and found that 76.21% performance was higher than comparison models.
Keywords
Deep Learning; Natural Language Processing; Semantic Role Labeling;
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