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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 2 Hits 1 – 20 of 34 1 Understanding the effects of negative (and positive) pointwise mutual information on word vectors Salle, A.; Villavicencio, A.. - : Taylor & Francis, 2022 BASE Show details 2 Improving tokenisation by alternative treatment of spaces Gow-Smith, E.; Scarton, C.; Madabushi, H.T.. - 2022 BASE Show details 3 Assessing idiomaticity representations in vector models with a noun compound dataset labeled at type and token levels Garcia, M.; Kramer Vieira, T.; Scarton, C.. - : Association for Computational Linguistics (ACL), 2021 BASE Show details 4 Probing for idiomaticity in vector space models Garcia, M.; Vieira, T.K.; Scarton, C.. - : Association for Computational Linguistics (ACL), 2021 BASE Show details 5 AStitchInLanguageModels : dataset and methods for the exploration of idiomaticity in pre-trained language models Tayyar Madabushi, H.; Gow-Smith, E.; Scarton, C.. - : Association for Computational Linguistics, 2021 BASE Show details 6 CogNLP-Sheffield at CMCL 2021 Shared Task: Blending cognitively inspired features with transformer-based language models for predicting eye tracking patterns Vickers, P.; Wainwright, R.; Tayyar Madabushi, H.. - : Association for Computational Linguistics (ACL), 2021 BASE Show details 7 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 8 Unsupervised compositionality prediction of nominal compounds Cordeiro, S.; Villavicencio, A.; Idiart, M.. - : MIT Press - Journals, 2019 BASE Show details 9 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 10 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 11 Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) Bansal, M.; Villavicencio, A.. - : Association for Computational Linguistics (ACL), 2019 BASE Show details 12 Discovering multiword expressions Villavicencio, A.; Idiart, M.. - : Cambridge University Press (CUP), 2019 BASE Show details 13 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 14 Unsupervised word segmentation from speech with attention Godard, P.; Boito, M.Z.; Ondel, L.. - : ISCA, 2018 BASE Show details 15 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 16 A corpus study of verbal multiword expressions in Brazilian Portuguese Ramisch, C.; Ramisch, R.; Zilio, L.. - : Springer International Publishing, 2018 BASE Show details 17 Unwritten languages demand attention too! Word discovery with encoder-decoder models Boito, M.Z.; Bérard, A.; Villavicencio, A.. - : IEEE, 2018 BASE Show details 18 Restricted recurrent neural tensor networks: Exploiting word frequency and compositionality Salle, A.; Villavicencio, A.. - : Association for Computational Linguistics, 2018 BASE Show details 19 UFRGS&LIF at SemEval-2016 task 10: Rule-based MWE identification and predominant-supersense tagging Cordeiro, S.R.; Ramisch, C.; Villavicencio, A.. - : Association for Computational Linguistics, 2016 BASE Show details 20 How naked is the naked truth? A multilingual lexicon of nominal compound compositionality Villavicencio, A.; Wilkens, R.; Ramisch, C.. - : Association for Computational Linguistics, 2016 BASE Show details Page: 1 2

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