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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 SemEval-2020 Task 3: Graded Word Similarity in Context ... The 28th International Conference on Computational Linguistics 2020; Armendariz, Carlos; Ljubešić, Nikola. - : Underline Science Inc., 2020 BASE Show details 2 Emergent Communication Pretraining for Few-Shot Machine Translation ... The 28th International Conference on Computational Linguistics 2020; Korhonen, Anna; Li, Yaoyiran. - : Underline Science Inc., 2020 BASE Show details 3 Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ... The 28th International Conference on Computational Linguistics 2020; Korhonen, Anna; Majewska, Olga. - : Underline Science Inc., 2020 BASE Show details 4 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ... The 28th International Conference on Computational Linguistics 2020; Glavaš, Goran; Korhonen, Anna; Ponzetto, Simone Paolo; Vulić, Ivan. - : Underline Science Inc., 2020 Abstract: Lexical entailment (LE) is a fundamental asymmetric lexico-semantic relation, supporting the hierarchies in lexical resources (e.g., WordNet, ConceptNet) and applications like natural language inference and taxonomy induction. Multilingual and cross-lingual NLP applications warrant models for LE detection that go beyond language boundaries. As part of SemEval 2020, we carried out a shared task (Task 2) on multilingual and cross-lingual LE. The shared task spans three dimensions: (1) monolingual LE in multiple languages versus cross-lingual LE, (2) binary versus graded LE, and (3) a set of 6 diverse languages (and 15 corresponding language pairs). We offered two different evaluation tracks: (a) distributional (Dist): for unsupervised, fully distributional models that capture LE solely on the basis of unannotated corpora, and (b) Any: for externally informed models, allowed to leverage any resources, including lexico-semantic networks (e.g., WordNet or BabelNet). In the Any track, we received system runs that ... Keyword: Computer and Information Science; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/2p49-kh89 https://underline.io/lecture/6409-semeval-2020-task-2-predicting-multilingual-and-cross-lingual-(graded)-lexical-entailment BASE Hide details 5 Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ... The 28th International Conference on Computational Linguistics 2020; Glavaš, Goran; Korhonen, Anna. - : Underline Science Inc., 2020 BASE Show details

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