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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 – 15 of 15 1 MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ... Liu, Qianchu; Liu, Fangyu; Collier, Nigel. - : arXiv, 2021 BASE Show details 2 MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ... Liu, Qianchu; Liu, Fangyu; Collier, Nigel. - : Apollo - University of Cambridge Repository, 2021 BASE Show details 3 MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Nigel; Korhonen, Anna. - : Underline Science Inc., 2021 BASE Show details 4 Context vs Target Word: Quantifying Biases in Lexical Semantic Datasets ... Liu, Qianchu; McCarthy, Diana; Korhonen, Anna. - : arXiv, 2021 Abstract: State-of-the-art contextualized models such as BERT use tasks such as WiC and WSD to evaluate their word-in-context representations. This inherently assumes that performance in these tasks reflect how well a model represents the coupled word and context semantics. This study investigates this assumption by presenting the first quantitative analysis (using probing baselines) on the context-word interaction being tested in major contextual lexical semantic tasks. Specifically, based on the probing baseline performance, we propose measures to calculate the degree of context or word biases in a dataset, and plot existing datasets on a continuum. The analysis shows most existing datasets fall into the extreme ends of the continuum (i.e. they are either heavily context-biased or target-word-biased) while only AM$^2$iCo and Sense Retrieval challenge a model to represent both the context and target words. Our case study on WiC reveals that human subjects do not share models' strong context biases in the dataset ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2112.06733 https://arxiv.org/abs/2112.06733 BASE Hide details 5 AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ... Liu, Qianchu; Ponti, Edoardo M.; McCarthy, Diana. - : arXiv, 2021 BASE Show details 6 AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Korhonen, Anna; Liu, Qianchu. - : Underline Science Inc., 2021 BASE Show details 7 Improving Machine Translation of Rare and Unseen Word Senses ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Dario; Fraser, Alexander. - : Underline Science Inc., 2021 BASE Show details 8 XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ... Ponti, Edoardo Maria; Glavaš, Goran; Majewska, Olga. - : arXiv, 2020 BASE Show details 9 XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ... Ponti, Edoardo; Glavaš, Goran; Majewska, Olga. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 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 BASE Show details 11 XCOPA: A multilingual dataset for causal commonsense reasoning Ponti, Edoardo Maria; Majewska, Olga; Liu, Qianchu. - : Association for Computational Linguistics, 2020 BASE Show details 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 BASE Show details 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 BASE Show details 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 BASE Show details 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 BASE Show details

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