Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach

Proceedings of the 28th International Conference on Computational Linguistics, pages 1396–1410 Barcelona, Spain (Online), December 8-13, 2020 1396 Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach Simone Conia and Roberto Navigli Sapienza NLP Group Department of Computer Science Sapienza University of Rome {conia,navigli}@di.uniroma1.it Abstract Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of-the-art mul- tilingual systems reveals the difficulty of bridging the wide gap in performance between high- resource (e.g., English) and low-resource (e.g., German) settings. To overcome this issue, we pro- pose a fully language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages. Our approach outperforms the state of the art in all the lan- guages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of training data is available. Our objective is not to reject approaches that rely on syntax, rather to set a strong and consistent language-independent baseline for future innovations in Semantic Role Labeling. We release our model code and checkpoints at https://github.com/SapienzaNLP/multi-srl. 1 Introduction Semantic Role Labeling (SRL) – the task of automatically addressing “Who did What to Whom, How, When and Where?” (Gildea and Jurafsky, 2000; M`arquez et al., 2008) – is a long standing open problem in Natural Language Processing (NLP), and a central task required to complete the puzzle of Natural Lan-guage Understanding (Navigli, 2018). Its roots date back to several decades ago, to when Fillmore (1968) first theorized the existence of deep semantic relations between a predicate and other sentential constituents. Over the years, different linguistic formalisms and their corresponding predicate-argument structure inventories expanded Fillmore’s seminal intuition (Dowty, 1991; Levin, 1993), yet the need to rely on manually designed complex feature templates severely limited early SRL models (Zhao et al., 2009). Fortunately, the recent great success of neural networks in NLP has drawn attention back to SRL and has led to considerable performance gains. This has been particularly the case for recurrent neural networks, thanks to their ability to better capture relations over sequences (Marcheggiani et al., 2017; He et al., 2017). The positive results obtained in SRL were rapidly extended to other fields where they proved to be beneficial to several downstream tasks, from Machine Translation (Marcheggiani et al., 2018) to Information Extraction (Christensen et al., 2011), Opinion Role Labeling (Zhang et al., 2019a), and Question Answering (He et al., 2015). As researchers constantly explored new approaches to improve SRL, the exploitation of syntactic features soon emerged as a natural choice. Cai and Lapata (2019b) suggested that syntax ought to help semantic role labelers since i) a significant portion of the predicate-argument relations in a semantic dependency graph mirrors the edges that appear in a syntactic dependency graph, and ii) there is often a deterministic mapping from syntactic to semantic roles. Following this line of thought, numerous papers from major venues reported improvements in SRL by explicitly taking advantage of different properties in syntactic …

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