Syntax-aware Multilingual Semantic Role Labeling
This work addresses the gap in multilingual SRL, which is important for natural language processing applications across diverse languages, though it appears incremental by building on existing syntactic integration approaches.
The paper tackled the problem of underdeveloped multilingual semantic role labeling (SRL) by proposing a syntax-aware method that integrates syntactic rules to prune arguments, achieving new state-of-the-art results on the CoNLL-2009 benchmarks for all seven languages tested.
Recently, semantic role labeling (SRL) has earned a series of success with even higher performance improvements, which can be mainly attributed to syntactic integration and enhanced word representation. However, most of these efforts focus on English, while SRL on multiple languages more than English has received relatively little attention so that is kept underdevelopment. Thus this paper intends to fill the gap on multilingual SRL with special focus on the impact of syntax and contextualized word representation. Unlike existing work, we propose a novel method guided by syntactic rule to prune arguments, which enables us to integrate syntax into multilingual SRL model simply and effectively. We present a unified SRL model designed for multiple languages together with the proposed uniform syntax enhancement. Our model achieves new state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages. Besides, we pose a discussion on the syntactic role among different languages and verify the effectiveness of deep enhanced representation for multilingual SRL.