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1
Delving Deeper into Cross-lingual Visual Question Answering ...
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2
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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3
Improving Word Translation via Two-Stage Contrastive Learning ...
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4
Specializing unsupervised pretraining models for word-level semantic similarity
Lauscher, Anne [Verfasser]; Vulic, Ivan [Verfasser]; Ponti, Edoardo Maria [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2021
DNB Subject Category Language
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5
Towards Zero-shot Language Modeling ...
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6
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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7
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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8
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification ...
Zhu, Yi; Shareghi, Ehsan; Li, Yingzhen. - : arXiv, 2021
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9
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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10
Parameter space factorization for zero-shot learning across tasks and languages ...
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11
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
Liu, Qianchu; Liu, Fangyu; Collier, Nigel. - : Apollo - University of Cambridge Repository, 2021
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12
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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13
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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14
Semantic Data Set Construction from Human Clustering and Spatial Arrangement ...
Majewska, Olga; McCarthy, Diana; Van Den Bosch, Jasper JF; Kriegeskorte, Nikolaus; Vulic, Ivan; Korhonen, Anna. - : Apollo - University of Cambridge Repository, 2021
Abstract: Abstract Research into representation learning models of lexical semantics usually utilizes some form of intrinsic evaluation to ensure that the learned representations reflect human semantic judgments. Lexical semantic similarity estimation is a widely used evaluation method, but efforts have typically focused on pairwise judgments of words in isolation, or are limited to specific contexts and lexical stimuli. There are limitations with these approaches that either do not provide any context for judgments, and thereby ignore ambiguity, or provide very specific sentential contexts that cannot then be used to generate a larger lexical resource. Furthermore, similarity between more than two items is not considered. We provide a full description and analysis of our recently proposed methodology for large-scale data set construction that produces a semantic classification of a large sample of verbs in the first phase, as well as multi-way similarity judgments made within the resultant semantic classes in the ...
URL: https://dx.doi.org/10.17863/cam.76712
https://www.repository.cam.ac.uk/handle/1810/329262
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15
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
Liu, Fangyu; Vulić, I; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2021
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16
Context vs Target Word: Quantifying Biases in Lexical Semantic Datasets ...
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17
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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18
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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19
Parameter space factorization for zero-shot learning across tasks and languages
In: Transactions of the Association for Computational Linguistics, 9 (2021)
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20
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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