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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 Page: 1 2 3 4 5 6 7 8 9 10...629 Hits 101 – 120 of 12.574 101 Grammatical Gender Effects on Cross-linguistic Categorization ... Sams, Christopher D.. - : Zenodo, 2020 BASE Show details 102 НОВЫЕ ПОДХОДЫ К ТИПОЛОГИИ ТЕРРИТОРИАЛЬНЫХ ВАРИАНТОВ ФРАНЦУЗСКОГО ЯЗЫКА ... : NEW APPROACHES TO THE TYPOLOGY OF TERRITORIAL VARIANTS OF THE FRENCH LANGUAGE ... О.И. Дагбаева; Е.Г. Дмитриева; З.А. Усманова. - : Мир науки, культуры, образования, 2020 BASE Show details 103 ПРИНИЦИПЫ КОГНИТИВНОГО МОДЕЛИРОВАНИЯ В ОБУЧЕНИИ ЯЗЫКУ ... : THE PRINCIPLES OF COGNITIVE MODELING IN LANGUAGE TEACHING ... Казарян Шушаник Ервандовна; Григорян Армине Эдуардовна. - : Право и практика, 2020 BASE Show details 104 The expression of speaker and nonspeaker surprise in South Conchucos Quechua Hintz, Daniel J.. - 2020 BASE Show details 105 Das Französische als exotische Sprache Haase, Martin. - : Otto-Friedrich-Universität, 2020. : Bamberg, 2020 BASE Show details 106 Das Französische als exotische Sprache Haase, Martin. - : Narr, 2020. : Tübingen, 2020 BASE Show details 107 Inductive Bias and Modular Design for Sample-Efficient Neural Language Learning Ponti, Edoardo. - : University of Cambridge, 2020. : St Johns, 2020 Abstract: Most of the world's languages suffer from the paucity of annotated data. This curbs the effectiveness of supervised learning, the most widespread approach to modelling language. Instead, an alternative paradigm could take inspiration from the propensity of children to acquire language from limited stimuli, in order to enable machines to learn any new language from a few examples. The abstract mechanisms underpinning this ability include 1) a set of in-born inductive biases and 2) the deep entrenchment of language in other perceptual and cognitive faculties, combined with the ability to transfer and recombine knowledge across these domains. The main contribution of my thesis is giving concrete form to both these intuitions. Firstly, I argue that endowing a neural network with the correct inductive biases is equivalent to constructing a prior distribution over its weights and its architecture (including connectivity patterns and non-linear activations). This prior is inferred by "reverse-engineering" a representative set of observed languages and harnessing typological features documented by linguists. Thus, I provide a unified framework for cross-lingual transfer and architecture search by recasting them as hierarchical Bayesian neural models. Secondly, the skills relevant to different language varieties and different tasks in natural language processing are deeply intertwined. Hence, the neural weights modelling the data for each of their combinations can be imagined as lying in a structured space. I introduce a Bayesian generative model of this space, which is factorised into latent variables representing each language and each task. By virtue of this modular design, predictions can generalise to unseen combinations by extrapolating from the data of observed combinations. The proposed models are empirically validated on a spectrum of language-related tasks (character-level language modelling, part-of-speech tagging, named entity recognition, and common-sense reasoning) and a typologically diverse sample of about a hundred languages. Compared to a series of competitive baselines, they achieve better performances in new languages in zero-shot and few-shot learning settings. In general, they hold promise to extend state-of-the-art language technology to under-resourced languages by means of sample efficiency and robustness to the cross-lingual variation. ; ERC (Consolidator Grant 648909) Lexical Google Research Faculty Award 2018 Keyword: Bayesian Models; Deep Learning; Inductive Bias; Linguistic Typology; Modularity; Multilingual Natural Language Processing; Neural Networks; Sample Efficiency; Systematic Generalisation URL: https://doi.org/10.17863/CAM.66424 https://www.repository.cam.ac.uk/handle/1810/319303 BASE Hide details 108 Overcoming Word-Centrism: Towards a New Foundation for the Philosophy of Language ; Преодолевая словоцентризм: на пути к новым основаниям философии языка Boroday, Sergey Yu.; Бородай, С.Ю.. - : Сибирский федеральный университет. Siberian Federal University, 2020 BASE Show details 109 Kinship and affinity in Indo-European Milanova, Veronika. - 2020 BASE Show details 110 Das Pronomen "sich" an der Schnittstelle von Reflexivität und Reziprozität Marinica, Catharina. - 2020 BASE Show details 111 Syntaktische Interferenzerscheinungen in mündlichem Deutsch als Fremdsprache Kogler, Stefan. - 2020 BASE Show details 112 Natural explanations for the history of word-final dental fricatives in English Nitsche, Ines. - 2020 BASE Show details 113 Kontrastive Linguistik als Mikrotypologie: Die Rolle des Deutschen als L3 Livio Gaeta. - : Peter Lang, 2020. : country:DEU, 2020. : place:Berlin, 2020 BASE Show details 114 Investigating differential case marking in Sümi, a language of Nagaland, using language documentation and experimental methods Teo, Amos. - : University of Oregon, 2020 BASE Show details 115 Comparability and measurement in typological science: The bright future for linguistics Round, Erich R.; Corbett, Greville G.. - : De Gruyter Mouton, 2020 BASE Show details 116 Semantic Categories of Artifacts and Animals Reflect Efficient Coding Zaslavsky, Noga; Regier, Terry; Tishby, Naftali... In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 117 Kalunga in the lusophone context: A phylogenetic study Ana Paulla Braga Mattos; Márcia Santos Duarte Oliveira In: Journal of Portuguese Linguistics, Vol 19, Iss 1 (2020) (2020) BASE Show details 118 Kalunga in the lusophone context: A phylogenetic study Ana Paulla Braga Mattos; Márcia Santos Duarte Oliveira In: Journal of Portuguese Linguistics, Vol 19, Iss 1 (2020) (2020) BASE Show details 119 Contextual Conditions and Constraints in Chinese Dangling Topics Jiayi Wu In: Acta Linguistica Asiatica, Vol 10, Iss 2 (2020) (2020) BASE Show details 120 Manner Verb Construction and Reduplication of Kedang Language: A Typological Study * Khasanah, Nurul; Subiyanto, Agus In: PAROLE: Journal of Linguistics and Education; Vol 10, No 2 (2020): Volume 10 Number 2 October 2020; 110-123 ; 23380683 ; 2087-345X (2020) BASE Show details Page: 1 2 3 4 5 6 7 8 9 10...629

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