Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 3269–3275 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 3269 Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration Simone Conia Roberto Navigli Sapienza NLP Group Department of Computer Science Sapienza University of Rome {conia,navigli}@di.uniroma1.it Abstract Recent studies treat Word Sense Disambigua- tion (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by ex- pert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for WSD, es- pecially if the sense inventory of choice is fine- grained. In this paper, we approach WSD as a multi-label classification problem in which multiple senses can be assigned to each target word. Not only does our simple method bear a closer resemblance to how human annota- tors disambiguate text, but it can also be ex- tended seamlessly to exploit structured knowl- edge from semantic networks to achieve state- of-the-art results in English all-words WSD. 1 Introduction Word Sense Disambiguation (WSD) is traditionally framed as the task of associating a word in con- text with its correct meaning from a finite set of possible choices (Navigli, 2009). Following this definition, recently proposed neural models were trained to maximize the probability of the most appropriate meaning while minimizing the proba- bility of the other possible choices (Huang et al., 2019; Vial et al., 2019; Blevins and Zettlemoyer, 2020; Bevilacqua and Navigli, 2020). Although this training objective proved to be extremely ef- fective and even led to Bevilacqua and Navigli (2020) reaching the estimated upper bound of inter- annotator agreement for WSD performance on the unified evaluation framework of Raganato et al. (2017b), adhering to it underplays a fundamental aspect of how human annotators disambiguate text. Indeed, past studies have observed that it is not uncommon for a word to have multiple appropri- ate meanings in a given context, meanings that can be used interchangeably under some circum- stances because their boundaries are not clear cut (Tuggy, 1993; Kilgarriff, 1997; Hanks, 2000; Erk and McCarthy, 2009). This is especially evident if the underlying sense inventory is fine-grained, as the complexity, and therefore performance, of WSD is tightly coupled to sense granularity (Lac- erra et al., 2020). The difficulty an annotator faces in choosing the most appropriate meaning from a fine-grained sense inventory becomes clear from an analysis of gold standard datasets: a non-negligible 5% of the target words are annotated with two or more sense labels in several gold standard datasets, including Senseval-2 (Edmonds and Cotton, 2001), Senseval-3 (Snyder and Palmer, 2004), SemEval- 2007 (Pradhan et al., 2007), SemEval-2013 (Nav- igli et al., 2013), and SemEval-2015 (Moro and Navigli, 2015). Therefore, we follow Erk and McCarthy (2009), Jurgens (2012), and Erk et al. (2013), and argue that forcing a system to treat WSD as a single-label classification …

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