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SimLex-999 Slovenian translation SimLex-999-sl 1.0
Pollak, Senja; Vulić, Ivan; Pelicon, Andraž. - : University of Ljubljana, 2021
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Towards Zero-shot Language Modeling ...
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Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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Modelling Latent Translations for Cross-Lingual Transfer ...
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Prix-LM: Pretraining for Multilingual Knowledge Base Construction ...
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Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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xGQA: Cross-Lingual Visual Question Answering ...
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On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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10
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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11
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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12
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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13
Parameter space factorization for zero-shot learning across tasks and languages ...
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14
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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UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
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How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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17
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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19
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
Abstract: Capturing word meaning in context and distinguishing between correspondences and variations across languages is key to building successful multilingual and cross-lingual text representation models. However, existing multilingual evaluation datasets that evaluate lexical semantics "in-context" have various limitations. In particular, 1) their language coverage is restricted to high-resource languages and skewed in favor of only a few language families and areas, 2) a design that makes the task solvable via superficial cues, which results in artificially inflated (and sometimes super-human) performances of pretrained encoders, on many target languages, which limits their usefulness for model probing and diagnostics, and 3) little support for cross-lingual evaluation. In order to address these gaps, we present AM2iCo (Adversarial and Multilingual Meaning in Context), a wide-coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation ... : EMNLP 2021 long paper ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2104.08639
https://dx.doi.org/10.48550/arxiv.2104.08639
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