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    • Villavicencio, A. (6)
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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 – 6 of 6 1 Unsupervised compositionality prediction of nominal compounds Cordeiro, S.; Villavicencio, A.; Idiart, M.. - : MIT Press - Journals, 2019 BASE Show details 2 A dual-attention hierarchical recurrent neural network for dialogue act classification Li, R.; Lin, C.; Collinson, M.. - : Association for Computational Linguistics (ACL), 2019 BASE Show details 3 When the whole is greater than the sum of its parts : multiword expressions and idiomaticity Villavicencio, A.. - : Association for Computational Linguistics, 2019 BASE Show details 4 Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) Bansal, M.; Villavicencio, A.. - : Association for Computational Linguistics (ACL), 2019 BASE Show details 5 Discovering multiword expressions Villavicencio, A.; Idiart, M.. - : Cambridge University Press (CUP), 2019 BASE Show details 6 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 Abstract: Since Bahdanau et al. [1] first introduced attention for neural machine translation, most sequence-to-sequence models made use of attention mechanisms [2, 3, 4]. While they produce soft-alignment matrices that could be interpreted as alignment between target and source languages, we lack metrics to quantify their quality, being unclear which approach produces the best alignments. This paper presents an empirical evaluation of 3 of the main sequence-to-sequence models for word discovery from unsegmented phoneme sequences: CNN, RNN and Transformer-based. This task consists in aligning word sequences in a source language with phoneme sequences in a target language, inferring from it word segmentation on the target side [5]. Evaluating word segmentation quality can be seen as an extrinsic evaluation of the soft-alignment matrices produced during training. Our experiments in a low-resource scenario on Mboshi and English languages (both aligned to French) show that RNNs surprisingly outperform CNNs and Transformer for this task. Our results are confirmed by an intrinsic evaluation of alignment quality through the use Average Normalized Entropy (ANE). Lastly, we improve our best word discovery model by using an alignment entropy confidence measure that accumulates ANE over all the occurrences of a given alignment pair in the collection. URL: http://eprints.whiterose.ac.uk/155716/ https://www.isca-speech.org/archive/Interspeech_2019/abstracts/2029.html http://eprints.whiterose.ac.uk/155716/8/Boito%20et%20al%202019%20Empirical%20Evaluation%20of%20Sequence-to-Sequence%20Models%20for%20Word%20Discovery,%20ISCA.pdf BASE Hide details

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