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The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing.

, and . SemEval@ACL, page 929-933. Association for Computational Linguistics, (2017)

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A Taxonomy for In-depth Evaluation of Normalization for User Generated Content., , and . LREC, European Language Resources Association (ELRA), (2018)Evaluating Scoped Meaning Representations., , , and . LREC, European Language Resources Association (ELRA), (2018)Exploring Neural Methods for Parsing Discourse Representation Structures., , , and . Trans. Assoc. Comput. Linguistics, (2018)Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations., and . CoRR, (2017)Controlling Topic-Focus Articulation in Meaning-to-Text Generation using Graph Neural Networks., , and . CoRR, (2023)Using Translated Data to Improve Deep Learning Author Profiling Models: Notebook for PAN at CLEF 2018., , , and . CLEF (Working Notes), volume 2125 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations., , and . COLING, page 4186-4192. International Committee on Computational Linguistics, (2022)The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages., , , and . CoRR, (2020)The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing., and . SemEval@ACL, page 929-933. Association for Computational Linguistics, (2017)UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish., , and . SemEval@NAACL-HLT, page 279-285. Association for Computational Linguistics, (2018)