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UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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Emergent Communication Pretraining for Few-Shot Machine Translation ...
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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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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 ...
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Keyword:
Computer and Information Science; Natural Language Processing; Neural Network
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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
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
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A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
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A deep learning approach to bilingual lexicon induction in the biomedical domain.
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Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine.
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