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A Metric Learning Approach to Misogyny Categorization.

, , , and . RepL4NLP@ACL, page 89-94. Association for Computational Linguistics, (2020)

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LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis., , , , and . SemEval@COLING, page 1281-1287. International Committee for Computational Linguistics, (2020)A Metric Learning Approach to Misogyny Categorization., , , and . RepL4NLP@ACL, page 89-94. Association for Computational Linguistics, (2020)The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools., , , , , , , and . LREC, page 1595-1602. European Language Resources Association, (2022)Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding., , , , , , , and . LREC, page 2949-2956. European Language Resources Association, (2022)End-To-End Named Entity And Semantic Concept Extraction From Speech., , , , , , and . SLT, page 692-699. IEEE, (2018)A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification., , , and . CoRR, (2020)Benchmarking Transformers-based models on French Spoken Language Understanding tasks., , , and . INTERSPEECH, page 1238-1242. ISCA, (2022)A Cooking Knowledge Graph and Benchmark for Question Answering Evaluation in Lifelong Learning Scenarios., , , , , and . NLDB, volume 12089 of Lecture Notes in Computer Science, page 94-101. Springer, (2020)Error Analysis Applied to End-to-End Spoken Language Understanding., , , , , , and . ICASSP, page 8514-8518. IEEE, (2020)Using Hypothesis Selection Based Features for Confusion Network MT System Combination., and . HyTra@EACL, page 2-6. The Association for Computer Linguistics, (2014)