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    • Villavicencio, A. (5)
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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 – 5 of 5 1 Unsupervised word segmentation from speech with attention Godard, P.; Boito, M.Z.; Ondel, L.. - : ISCA, 2018 BASE Show details 2 Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks Paula, F.; Wilkens, R.; Idiart, M.. - : Association for Computational Linguistics, 2018 BASE Show details 3 A corpus study of verbal multiword expressions in Brazilian Portuguese Ramisch, C.; Ramisch, R.; Zilio, L.. - : Springer International Publishing, 2018 BASE Show details 4 Unwritten languages demand attention too! Word discovery with encoder-decoder models Boito, M.Z.; Bérard, A.; Villavicencio, A.. - : IEEE, 2018 BASE Show details 5 Restricted recurrent neural tensor networks: Exploiting word frequency and compositionality Salle, A.; Villavicencio, A.. - : Association for Computational Linguistics, 2018 Abstract: Increasing the capacity of recurrent neural networks (RNN) usually involves augmenting the size of the hidden layer, with significant increase of computational cost. Recurrent neural tensor networks (RNTN) increase capacity using distinct hidden layer weights for each word, but with greater costs in memory usage. In this paper, we introduce restricted recurrent neural tensor networks (r-RNTN) which reserve distinct hidden layer weights for frequent vocabulary words while sharing a single set of weights for infrequent words. Perplexity evaluations show that for fixed hidden layer sizes, r-RNTNs improve language model performance over RNNs using only a small fraction of the parameters of unrestricted RNTNs. These results hold for r-RNTNs using Gated Recurrent Units and Long Short-Term Memory. URL: http://eprints.whiterose.ac.uk/153558/ BASE Hide details

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