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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ... Razumovskaia, Evgeniia; Glavaš, Goran; Majewska, Olga. - : arXiv, 2021 BASE Show details 2 Parameter space factorization for zero-shot learning across tasks and languages ... Ponti, Edoardo M.; Vulić, Ivan; Cotterell, Ryan. - : ETH Zurich, 2021 BASE Show details 3 AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ... Liu, Qianchu; Ponti, Edoardo M.; McCarthy, Diana. - : arXiv, 2021 BASE Show details 4 Parameter space factorization for zero-shot learning across tasks and languages Parovic, Marinela; Cotterell, Ryan; Vulić, Ivan; Korhonen, Anna; Ponti, Edoardo M.; Reichart, Roi 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 BASE Hide details 5 Emergent Communication Pretraining for Few-Shot Machine Translation ... Li, Yaoyiran; Ponti, Edoardo M.; Vulić, Ivan. - : arXiv, 2020 BASE Show details 6 SIGTYP 2020 Shared Task: Prediction of Typological Features ... Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.. - : arXiv, 2020 BASE Show details 7 Verb Knowledge Injection for Multilingual Event Processing ... Majewska, Olga; Vulić, Ivan; Glavaš, Goran. - : arXiv, 2020 BASE Show details

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