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1
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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2
Probing Pretrained Language Models for Lexical Semantics ...
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3
Specializing unsupervised pretraining models for word-level semantic similarity
Ponti, Edoardo Maria; Korhonen, Anna; Vulić, Ivan. - : Association for Computational Linguistics, ACL, 2020
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4
Probing pretrained language models for lexical semantics
Vulić, Ivan; Korhonen, Anna; Litschko, Robert. - : Association for Computational Linguistics, 2020
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5
XCOPA: A multilingual dataset for causal commonsense reasoning
Ponti, Edoardo Maria; Majewska, Olga; Liu, Qianchu. - : Association for Computational Linguistics, 2020
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6
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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7
Specializing distributional vectors of all words for lexical entailment
Ponti, Edoardo Maria; Kamath, Aishwarya; Pfeiffer, Jonas. - : Association for Computational Linguistics, 2019
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8
Cross-lingual semantic specialization via lexical relation induction
Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2019
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9
Informing unsupervised pretraining with external linguistic knowledge
Lauscher, Anne; Vulić, Ivan; Ponti, Edoardo Maria. - : Cornell University, 2019
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10
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
Abstract: Semantic specialization is the process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space. We exploit words seen in the resources as training examples for learning a global specialization function. This function is learned by combining a standard L2-distance loss with an adversarial loss: the adversarial component produces more realistic output vectors. We show the effectiveness and robustness of the proposed method across three ... : Accepted at EMNLP 2018 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1809.04163
https://arxiv.org/abs/1809.04163
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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
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