Alonso-Alonso, IagoVilares, DavidGómez-Rodríguez, Carlos2024-05-232024-05-232022-07Iago Alonso-Alonso, David Vilares, and Carlos Gómez-Rodríguez. 2022. LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1389–1400, Seattle, United States. Association for Computational Linguistics.http://hdl.handle.net/2183/36586Held 14-15 July 2022, Online[Absctract]: This paper addressed the problem of structured sentiment analysis using a bi-affine semantic dependency parser, large pre-trained language models, and publicly available translation models. For the monolingual setup, we considered: (i) training on a single treebank, and (ii) relaxing the setup by training on treebanks coming from different languages that can be adequately processed by cross-lingual language models. For the zero-shot setup and a given target treebank, we relied on: (i) a word-level translation of available treebanks in other languages to get noisy, unlikely-grammatical, but annotated data (we release as much of it as licenses allow), and (ii) merging those translated treebanks to obtain training data. In the post-evaluation phase, we also trained cross-lingual models that simply merged all the English treebanks and did not use word-level translations, and yet obtained better results. According to the official results, we ranked 8th and 9th in the monolingual and cross-lingual setups.engAtribución 3.0 Españahttp://creativecommons.org/licenses/by/3.0/es/Structured Sentiment AnalysisCross-Lingual Language ModelsSemantic Dependency ParsingZero-Shot Learningconference outputopen access