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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 – 8 of 8 1 Towards Zero-shot Language Modeling ... Ponti, Edoardo Maria; Vulić, Ivan; Cotterell, Ryan. - : arXiv, 2021 BASE Show details 2 Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ... Razumovskaia, Evgeniia; Glavaš, Goran; Majewska, Olga. - : arXiv, 2021 BASE Show details 3 Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ... Liu, Fangyu; Vulić, Ivan; Korhonen, Anna. - : arXiv, 2021 BASE Show details 4 Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification ... Zhu, Yi; Shareghi, Ehsan; Li, Yingzhen. - : arXiv, 2021 BASE Show details 5 MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ... Liu, Qianchu; Liu, Fangyu; Collier, Nigel; Korhonen, Anna; Vulić, Ivan. - : arXiv, 2021 Abstract: Recent work indicated that pretrained language models (PLMs) such as BERT and RoBERTa can be transformed into effective sentence and word encoders even via simple self-supervised techniques. Inspired by this line of work, in this paper we propose a fully unsupervised approach to improving word-in-context (WiC) representations in PLMs, achieved via a simple and efficient WiC-targeted fine-tuning procedure: MirrorWiC. The proposed method leverages only raw texts sampled from Wikipedia, assuming no sense-annotated data, and learns context-aware word representations within a standard contrastive learning setup. We experiment with a series of standard and comprehensive WiC benchmarks across multiple languages. Our proposed fully unsupervised MirrorWiC models obtain substantial gains over off-the-shelf PLMs across all monolingual, multilingual and cross-lingual setups. Moreover, on some standard WiC benchmarks, MirrorWiC is even on-par with supervised models fine-tuned with in-task data and sense labels. ... : CoNLL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2109.09237 https://arxiv.org/abs/2109.09237 BASE Hide details 6 Context vs Target Word: Quantifying Biases in Lexical Semantic Datasets ... Liu, Qianchu; McCarthy, Diana; Korhonen, Anna. - : arXiv, 2021 BASE Show details 7 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 8 Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ... Liu, Fangyu; Vulić, Ivan; Korhonen, Anna. - : arXiv, 2021 BASE Show details

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