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EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification ...
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Delving Deeper into Cross-lingual Visual Question Answering ...
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3
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ...
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4
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ...
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5
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ...
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6
Improving Word Translation via Two-Stage Contrastive Learning ...
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7
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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9
Towards Zero-shot Language Modeling ...
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10
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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11
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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12
Modelling Latent Translations for Cross-Lingual Transfer ...
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13
Prix-LM: Pretraining for Multilingual Knowledge Base Construction ...
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14
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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15
xGQA: Cross-Lingual Visual Question Answering ...
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16
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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17
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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18
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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19
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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20
Parameter space factorization for zero-shot learning across tasks and languages ...
Abstract: Most combinations of NLP tasks and language varieties lack in-domain examples for supervised training because of the paucity of annotated data. How can neural models make sample-efficient generalizations from task–language combinations with available data to low-resource ones? In this work, we propose a Bayesian generative model for the space of neural parameters. We assume that this space can be factorized into latent variables for each language and each task. We infer the posteriors over such latent variables based on data from seen task–language combinations through variational inference. This enables zero-shot classification on unseen combinations at prediction time. For instance, given training data for named entity recognition (NER) in Vietnamese and for part-of-speech (POS) tagging in Wolof, our model can perform accurate predictions for NER in Wolof. In particular, we experiment with a typologically diverse sample of 33 languages from 4 continents and 11 families, and show that our model yields ... : Transactions of the Association for Computational Linguistics, 9 ...
URL: https://dx.doi.org/10.3929/ethz-b-000498270
http://hdl.handle.net/20.500.11850/498270
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