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    • Besacier, L. (4)
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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 – 4 of 4 1 Investigating language impact in bilingual approaches for computational language documentation Boito, M.Z.; Villavicencio, A.; Besacier, L.. - : Special Interest Group: Under-resourced Languages (SIGUL), 2020 BASE Show details 2 Empirical evaluation of sequence-to-sequence models for word discovery in low-resource settings Boito, M.Z.; Villavicencio, A.; Besacier, L.. - : International Speech Communication Association (ISCA), 2019 BASE Show details 3 Unsupervised word segmentation from speech with attention Godard, P.; Boito, M.Z.; Ondel, L.. - : ISCA, 2018 BASE Show details 4 Unwritten languages demand attention too! Word discovery with encoder-decoder models Boito, M.Z.; Bérard, A.; Villavicencio, A.; Besacier, L.. - : IEEE, 2018 Abstract: Word discovery is the task of extracting words from un-segmented text. In this paper we examine to what extent neural networks can be applied to this task in a realistic unwritten language scenario, where only small corpora and limited annotations are available. We investigate two scenarios: one with no supervision and another with limited supervision with access to the most frequent words. Obtained results show that it is possible to retrieve at least 27% of the gold standard vocabulary by training an encoder-decoder neural machine translation system with only 5,157 sentences. This result is close to those obtained with a task-specific Bayesian nonparametric model. Moreover, our approach has the advantage of generating translation alignments, which could be used to create a bilingual lexicon. As a future perspective, this approach is also well suited to work directly from speech. URL: http://eprints.whiterose.ac.uk/153555/ BASE Hide details

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