Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 2634–2641 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 2634 Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing Riccardo Orlando, Simone Conia, Stefano Faralli, Roberto Navigli Sapienza NLP Sapienza University of Rome Piazzale Aldo Moro, 5, 00185 Rome, Italy {orlando,navigli}@diag.uniroma1.it, {conia,faralli}@di.uniroma1.it Abstract In this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing. Together, such annotations can be used to provide users with rich and diverse semantic information, help second-language learners, and allow researchers to integrate explicit semantic knowledge into downstream tasks and real-world applications. Keywords: Multilingualism and Language Technology for All, Word Sense Disambiguation, Semantic Role Labeling, Semantic Parsing, Multilinguality, API 1. Introduction The grand goal of Natural Language Processing (NLP) is to realise automatic systems that are able to under- stand language. In order to do so, NLP combines Lin- guistics with Machine Learning – in particular Deep Learning – to develop increasingly “intelligent” sys- tems that are able to process, understand, and gener- ate natural language, as opposed to artificial language (Otter et al., 2021). Despite the tremendous progress that we have witnessed in recent years, NLP systems are still a long way from truly understanding what they process, i.e., researchers are still striving to come closer to true Natural Language Understanding (NLU). In- deed, current approaches still struggle to obtain hu- man performance on many complex tasks that involve identifying and extracting the meaning conveyed by texts (Navigli, 2018). This is true especially for those tasks that require explicit semantic knowledge that hu- man beings usually acquire by experiencing the real world (Bender and Koller, 2020); for example, asso- ciating the correct meaning to a word in context, un- derstanding agentive-patientive relations between sen- tential constituents, and representing the interplay be- tween different concepts. Even if we are still far from achieving true NLU, ex- plicit semantic knowledge is already being employed with success in an increasingly growing body of appli- cations spanning multiple areas of AI including NLP with Information Retrieval (Christensen et al., 2010; Blloshmi et al., 2021b), Question Answering (He et al., 2015; Ramakrishnan et al., 2003), Text Summarization (Hardy and Vlachos, 2018; Liao et al., 2018), and Ma- chine Translation (Marcheggiani et al., 2018; Raganato et al., 2019; Song et al., 2019), but also Computer Vi- sion with Visual Semantic Role Labeling (Gupta and Malik, 2015), Situation Recognition (Yatskar et al., 2016), and Video Understanding (Sadhu et al., 2021), inter alia. One important downside of such progress is that the complexity reached by current techniques has become a significant issue: understanding how a state- of-the-art system works now requires expert knowledge of linguistic theories and Deep Learning methods. This issue is exacerbated when dealing with multiple lan- guages, since learning to transfer …

Content truncated to 500 words