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EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification ...
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Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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3
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
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Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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Improving Word Translation via Two-Stage Contrastive Learning ...
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On cross-lingual retrieval with multilingual text encoders
Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer Science + Business Media, 2022
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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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9
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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10
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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11
Modelling Latent Translations for Cross-Lingual Transfer ...
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12
Prix-LM: Pretraining for Multilingual Knowledge Base Construction ...
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13
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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xGQA: Cross-Lingual Visual Question Answering ...
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15
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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16
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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
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17
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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18
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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19
Parameter space factorization for zero-shot learning across tasks and languages ...
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20
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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