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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...201 202 203 204 205 Hits 4.081 – 4.089 of 4.089 4081 Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions Gong, Rong; Serra, Xavier. - : International Speech Communication Association (ISCA) BASE Show details 4082 Neural basis of bilingual language control Calabria, Marco; Costa, Albert, 1970-; Green, David W.. - : Wiley BASE Show details 4083 SignON: Bridging the gap between sign and spoken languages Saggion, Horacio; Shterionov, Dimitar; Labaka, Gorka. - : CEUR Workshop Proceedings BASE Show details 4084 PunkProse [software] Öktem, Alp. - : Universitat Pompeu Fabra BASE Show details 4085 Multiplicative and additive modulation of neuronal tuning with population activity affects encoded information Arandía Romero, Iñigo; Tanabe, Seiji; Drugowitsch, Jan. - : Elsevier BASE Show details 4086 How to represent a word and predict it, too: improving tied architectures for language modelling Boleda, Gemma; Aina, Laura; Gulordava, Kristina. - : ACL (Association for Computational Linguistics) BASE Show details 4087 Convolutional neural network language models Boleda, Gemma; Pham, Nghia The; Kruszewski, German. - : ACL (Association for Computational Linguistics) BASE Show details 4088 Linguistic generalization and compositionality in modern artificial neural networks. Baroni, Marco. - : Royal Society Abstract: In the last decade, deep artificial neural networks have achieved astounding performance in many natural language processing tasks. Given the high productivity of language, these models must possess e ective generalization abilities. It is widely assumed that humans handle linguistic productivity by means of algebraic compositional rules: Are deep networks similarly compositional? After reviewing the main innovations characterizing current deep language processing networks, I discuss a set of studies suggesting that deep networks are capable of subtle grammar-dependent generalizations, but also that they do not rely on systematic compositional rules. I argue that the intriguing behaviour of these devices (still awaiting a full understanding) should be of interest to linguists and cognitive scientists, as it o ers a new perspective on possible computational strategies to deal with linguistic productivity beyond rule-based compositionality, and it might lead to new insights into the less systematic generalization patterns that also appear in natural language. Keyword: Artificial neural networks; Compositionality; Deep learning; Linguistic productivity URL: https://doi.org/10.1098/rstb.2019.0307 http://hdl.handle.net/10230/43459 BASE Hide details 4089 LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using regression and convolutions for cross-document semantic linking and summarization of scholarly literature Bravo Serrano, Àlex, 1984-; Chiruzzo, Luis; Saggion, Horacio. - : CEUR Workshop Proceedings BASE Show details Page: 1...201 202 203 204 205

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