• Refine your search:
  • 
    Keyword
  • 
    Creator / Publisher:
    
    • Goldwater, Sharon (59)
    • Macquarie University. Department of Computing (14)
    • Johnson, Mark (13)
    • Kamper, Herman (10)
    • Dupoux, Emmanuel (7)
    • Schatz, Thomas (7)
    • Matusevych, Yevgen (6)
    • Engineering and Physical Sciences Research Council (EPSRC) (5)
    • Feldman, Naomi H. (5)
    • Griffiths, Thomas L. (5)
    •   more
  • 
    Year
  • 
    Medium
  • 
    Type
  • 
    BLLDB-Access

Search in the Catalogues and Directories

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 Hits 1 – 20 of 59 1 Universal Dependencies and Semantics for English and Hebrew Child-directed Speech Szubert, Ida; Abend, Omri; Schneider, Nathan... In: Proceedings of the Society for Computation in Linguistics (2022) BASE Show details 2 Do Infants Really Learn Phonetic Categories? Feldman, Naomi; Goldwater, Sharon; Dupoux, Emmanuel... In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-03550830 ; Open Mind, MIT Press, 2021, 5, pp.113-131. ⟨10.1162/opmi_a_00046⟩ (2021) BASE Show details 3 Early phonetic learning without phonetic categories -- Insights from large-scale simulations on realistic input Schatz, Thomas; Feldman, Naomi,; Goldwater, Sharon... In: ISSN: 0027-8424 ; EISSN: 1091-6490 ; Proceedings of the National Academy of Sciences of the United States of America ; https://hal.archives-ouvertes.fr/hal-03070566 ; Proceedings of the National Academy of Sciences of the United States of America , National Academy of Sciences, 2021, 118 (7), pp.e2001844118. ⟨10.1073/pnas.2001844118⟩ (2021) BASE Show details 4 Black or White but never neutral: How readers perceive identity from yellow or skin-toned emoji ... Robertson, Alexander; Magdy, Walid; Goldwater, Sharon. - : arXiv, 2021 BASE Show details 5 A phonetic model of non-native spoken word processing ... Matusevych, Yevgen; Kamper, Herman; Schatz, Thomas. - : arXiv, 2021 BASE Show details 6 Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech ... Szubert, Ida; Abend, Omri; Schneider, Nathan. - : arXiv, 2021 BASE Show details 7 Do Infants Really Learn Phonetic Categories? Feldman, Naomi H.; Goldwater, Sharon; Dupoux, Emmanuel... In: Open Mind (Camb) (2021) BASE Show details 8 Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input Schatz, Thomas; Feldman, Naomi H.; Goldwater, Sharon... In: Proc Natl Acad Sci U S A (2021) BASE Show details 9 Multilingual acoustic word embedding models for processing zero-resource languages ... Kamper, Herman; Matusevych, Yevgen; Goldwater, Sharon. - : arXiv, 2020 BASE Show details 10 Improved acoustic word embeddings for zero-resource languages using multilingual transfer ... Kamper, Herman; Matusevych, Yevgen; Goldwater, Sharon. - : arXiv, 2020 BASE Show details 11 Analyzing autoencoder-based acoustic word embeddings ... Matusevych, Yevgen; Kamper, Herman; Goldwater, Sharon. - : arXiv, 2020 BASE Show details 12 Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals ... McCurdy, Kate; Goldwater, Sharon; Lopez, Adam. - : arXiv, 2020 BASE Show details 13 Evaluating computational models of infant phonetic learning across languages ... Matusevych, Yevgen; Schatz, Thomas; Kamper, Herman. - : arXiv, 2020 BASE Show details 14 Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages Hermann, Enno; Kamper, Herman; Goldwater, Sharon In: http://infoscience.epfl.ch/record/277105 (2020) BASE Show details 15 On understanding character-level models for representing morphology Vania, Clara. - : The University of Edinburgh, 2020 Abstract: Morphology is the study of how words are composed of smaller units of meaning (morphemes). It allows humans to create, memorize, and understand words in their language. To process and understand human languages, we expect our computational models to also learn morphology. Recent advances in neural network models provide us with models that compose word representations from smaller units like word segments, character n-grams, or characters. These so-called subword unit models do not explicitly model morphology yet they achieve impressive performance across many multilingual NLP tasks, especially on languages with complex morphological processes. This thesis aims to shed light on the following questions: (1) What do subword unit models learn about morphology? (2) Do we still need prior knowledge about morphology? (3) How do subword unit models interact with morphological typology? First, we systematically compare various subword unit models and study their performance across language typologies. We show that models based on characters are particularly effective because they learn orthographic regularities which are consistent with morphology. To understand which aspects of morphology are not captured by these models, we compare them with an oracle with access to explicit morphological analysis. We show that in the case of dependency parsing, character-level models are still poor in representing words with ambiguous analyses. We then demonstrate how explicit modeling of morphology is helpful in such cases. Finally, we study how character-level models perform in low resource, cross-lingual NLP scenarios, whether they can facilitate cross-linguistic transfer of morphology across related languages. While we show that cross-lingual character-level models can improve low-resource NLP performance, our analysis suggests that it is mostly because of the structural similarities between languages and we do not yet find any strong evidence of crosslinguistic transfer of morphology. This thesis presents a careful, in-depth study and analyses of character-level models and their relation to morphology, providing insights and future research directions on building morphologically-aware computational NLP models. Keyword: character-level models; dependency parsing; morphemes; morphology; natural language processing; NLP URL: https://doi.org/10.7488/era/49 https://hdl.handle.net/1842/36742 BASE Hide details 16 Methods for morphology learning in low(er)-resource scenarios Bergmanis, Toms. - : The University of Edinburgh, 2020 BASE Show details 17 Discovering and analysing lexical variation in social media text Shoemark, Philippa Jane. - : The University of Edinburgh, 2020 BASE Show details 18 Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection ... Corkery, Maria; Matusevych, Yevgen; Goldwater, Sharon. - : arXiv, 2019 BASE Show details 19 Analyzing ASR pretraining for low-resource speech-to-text translation ... Stoian, Mihaela C.; Bansal, Sameer; Goldwater, Sharon. - : arXiv, 2019 BASE Show details 20 Low-resource speech translation Bansal, Sameer. - : The University of Edinburgh, 2019 BASE Show details Page: 1 2 3

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 5 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 0 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 4 BDSL 0 IDS Bibliografie zur deutschen Grammatik 0 IDS Bibliografie zur Gesprächsforschung 0 IDS Konnektoren im Deutschen 0 IDS Präpositionen im Deutschen 0 IDS OBELEX meta 0 MPI-SHH Linguistics Collection 0 MPI for Psycholinguistics 1 Linked Open Data catalogues Annohub 0 Online resources Link directory 0 Journal directory 0 Database directory 0 Dictionary directory 0 Open access documents BASE 52 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0