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1
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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2
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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3
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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4
Context vs Target Word: Quantifying Biases in Lexical Semantic Datasets ...
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5
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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6
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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7
Improving Machine Translation of Rare and Unseen Word Senses ...
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8
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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9
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
Ponti, Edoardo; Glavaš, Goran; Majewska, Olga. - : Apollo - University of Cambridge Repository, 2020
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10
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
Liu, Qianchu; Korhonen, Anna-Leena; Majewska, Olga. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020
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11
XCOPA: A multilingual dataset for causal commonsense reasoning
Ponti, Edoardo Maria; Majewska, Olga; Liu, Qianchu. - : Association for Computational Linguistics, 2020
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12
Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation ...
Liu, Qianchu; McCarthy, D; Vulić, I. - : Apollo - University of Cambridge Repository, 2019
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13
Second-order contexts from lexical substitutes for few-shot learning of word representations ...
Liu, Qianchu; McCarthy, D; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2019
Abstract: There is a growing awareness of the need to handle rare and unseen words in word representation modelling. In this paper, we focus on few-shot learning of emerging concepts that fully exploits only a few available contexts. We introduce a substitute-based context representation technique that can be applied on an existing word embedding space. Previous context-based approaches to modelling unseen words only consider bag-of-word firstorder contexts, whereas our method aggregates contexts as second-order substitutes that are produced by a sequence-aware sentence completion model. We experimented with three tasks that aim to test the modelling of emerging concepts. We found that these tasks show different emphasis on first and second order contexts, and our substitute-based method achieved superior performance on naturallyoccurring contexts from corpora. ...
URL: https://dx.doi.org/10.17863/cam.44054
https://www.repository.cam.ac.uk/handle/1810/297013
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14
Second-order contexts from lexical substitutes for few-shot learning of word representations
Liu, Qianchu; McCarthy, D; Korhonen, Anna-Leena. - : *SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics, 2019
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15
Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation
Liu, Qianchu; McCarthy, D; Vulić, I. - : CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference, 2019
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