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21
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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22
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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23
Parameter space factorization for zero-shot learning across tasks and languages ...
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24
Analogy Training Multilingual Encoders ...
Garneau, Nicolas; Hartmann, Mareike; Sandholm, Anders. - : Apollo - University of Cambridge Repository, 2021
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25
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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26
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
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27
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
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28
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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29
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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30
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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31
Semantic Data Set Construction from Human Clustering and Spatial Arrangement ...
Majewska, Olga; McCarthy, Diana; Van Den Bosch, Jasper JF. - : Apollo - University of Cambridge Repository, 2021
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32
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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33
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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34
Parameter space factorization for zero-shot learning across tasks and languages
In: Transactions of the Association for Computational Linguistics, 9 (2021)
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 comparable or better results than state-of-the-art, zero-shot cross-lingual transfer methods. Our code is available at github.com/cambridgeltl/parameter-factorization. ; ISSN:2307-387X
URL: https://doi.org/10.3929/ethz-b-000498270
https://hdl.handle.net/20.500.11850/498270
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35
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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36
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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37
LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
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38
Verb Knowledge Injection for Multilingual Event Processing ...
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39
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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40
Is supervised syntactic parsing beneficial for language understanding tasks? An empirical investigation
Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2021
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