Papers › Syntax-driven Approach for Semantic Role Labeling

Syntax-driven Approach for Semantic Role Labeling

1 Jun 2022LREC 2022 6archive 2025-07-28

Yuanhe Tian, Han Qin, Fei Xia, Yan Song

As an important task to analyze the semantic structure of a sentence, semantic role labeling (SRL) aims to locate the semantic role (e.g., agent) of noun phrases with respect to a given predicate and thus plays an important role in downstream tasks such as dialogue systems. To achieve a better performance in SRL, a model is always required to have a good understanding of the context information. Although one can use advanced text encoder (e.g., BERT) to capture the context information, extra resources are also required to further improve the model performance. Considering that there are correlations between the syntactic structure and the semantic structure of the sentence, many previous studies leverage auto-generated syntactic knowledge, especially the dependencies, to enhance the modeling of context information through graph-based architectures, where limited attention is paid to other types of auto-generated knowledge. In this paper, we propose map memories to enhance SRL by encoding different types of auto-generated syntactic knowledge (i.e., POS tags, syntactic constituencies, and word dependencies) obtained from off-the-shelf toolkits. Experimental results on two English benchmark datasets for span-style SRL (i.e., CoNLL-2005 and CoNLL-2012) demonstrate the effectiveness of our approach, which outperforms strong baselines and achieves state-of-the-art results on CoNLL-2005.

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Tasks

POSSemantic Role LabelingSentence

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Role Labeling CoNLL 2005 SRL-MM + XLNet F1 89.80 #2 of 15 Archive leaderboard report
Semantic Role Labeling OntoNotes SRL-MM + XLNet F1 87.67 #5 of 17 Archive leaderboard report

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