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Embracing the messy and situated practice of dance technology design.

, , , and . Conference on Designing Interactive Systems, page 1383-1397. ACM, (2023)

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XNMT: The eXtensible Neural Machine Translation Toolkit., , , , , , , , , and 3 other author(s). AMTA (1), page 185-192. Association for Machine Translation in the Americas, (2018)Controlling Utterance Length in NMT-based Word Segmentation with Attention., , and . IWSLT, Association for Computational Linguistics, (2019)Unsupervised word discovery for computational language documentation. (Découverte non-supervisée de mots pour outiller la linguistique de terrain).. University of Paris-Saclay, France, (2019)Unsupervised Word Segmentation from Speech with Attention., , , , , , and . INTERSPEECH, page 2678-2682. ISCA, (2018)Unsupervised Learning of Word Segmentation: Does Tone Matter?, , , , and . CICLing (1), volume 13396 of Lecture Notes in Computer Science, page 349-359. Springer, (2018)Bayesian Models for Unit Discovery on a Very Low Resource Language., , , , , , , , , and . ICASSP, page 5939-5943. IEEE, (2018)Embracing the messy and situated practice of dance technology design., , , and . Conference on Designing Interactive Systems, page 1383-1397. ACM, (2023)Preliminary Experiments on Unsupervised Word Discovery in Mboshi., , , , , , , , , and . INTERSPEECH, page 3539-3543. ISCA, (2016)