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Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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Parameter space factorization for zero-shot learning across tasks and languages ...
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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 ...
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URL: https://dx.doi.org/10.3929/ethz-b-000498270 http://hdl.handle.net/20.500.11850/498270
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MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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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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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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Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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Semantic Data Set Construction from Human Clustering and Spatial Arrangement ...
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AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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Parameter space factorization for zero-shot learning across tasks and languages
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
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Verb Knowledge Injection for Multilingual Event Processing ...
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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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Is supervised syntactic parsing beneficial for language understanding tasks? An empirical investigation
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