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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 – 11 of 11 1 XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ... Ponti, Edoardo Maria; Glavaš, Goran; Majewska, Olga. - : arXiv, 2020 BASE Show details 2 Probing Pretrained Language Models for Lexical Semantics ... Vulić, Ivan; Ponti, Edoardo Maria; Litschko, Robert. - : arXiv, 2020 BASE Show details 3 Specializing unsupervised pretraining models for word-level semantic similarity Ponti, Edoardo Maria; Korhonen, Anna; Vulić, Ivan. - : Association for Computational Linguistics, ACL, 2020 BASE Show details 4 Probing pretrained language models for lexical semantics Vulić, Ivan; Korhonen, Anna; Litschko, Robert. - : Association for Computational Linguistics, 2020 BASE Show details 5 XCOPA: A multilingual dataset for causal commonsense reasoning Ponti, Edoardo Maria; Majewska, Olga; Liu, Qianchu. - : Association for Computational Linguistics, 2020 BASE Show details 6 Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ... Lauscher, Anne; Vulić, Ivan; Ponti, Edoardo Maria; Korhonen, Anna; Glavaš, Goran. - : arXiv, 2019 Abstract: Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, they encode only the distributional knowledge available in raw text corpora, incorporated through language modeling objectives. In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining. To this end, we generalize the standard BERT model to a multi-task learning setting where we couple BERT's masked language modeling and next sentence prediction objectives with an auxiliary task of binary word relation classification. Our experiments suggest that our "Lexically Informed" BERT (LIBERT), specialized for the word-level semantic similarity, yields better performance than the lexically blind "vanilla" BERT on several language understanding tasks. Concretely, LIBERT ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1909.02339 https://dx.doi.org/10.48550/arxiv.1909.02339 BASE Hide details 7 Specializing distributional vectors of all words for lexical entailment Ponti, Edoardo Maria; Kamath, Aishwarya; Pfeiffer, Jonas. - : Association for Computational Linguistics, 2019 BASE Show details 8 Cross-lingual semantic specialization via lexical relation induction Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2019 BASE Show details 9 Informing unsupervised pretraining with external linguistic knowledge Lauscher, Anne; Vulić, Ivan; Ponti, Edoardo Maria. - : Cornell University, 2019 BASE Show details 10 Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ... Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : arXiv, 2018 BASE Show details 11 Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018 BASE Show details

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