most citedControlling Utterance Length in NMT-based Word Segmentation with Attention

2 citations · 2 across the 1 of their papers we have counts for

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5 papers

cs.CL20192 cited

Controlling Utterance Length in NMT-based Word Segmentation with Attention

Pierre Godard, Laurent Besacier, Francois Yvon

One of the basic tasks of computational language documentation (CLD) is to identify word boundaries in an unsegmented phonemic stream. While several unsupervised monolingual word s…

cs.CL2018

Unsupervised Word Segmentation from Speech with Attention

Pierre Godard, Marcely Zanon-Boito, Lucas Ondel +4

We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource,…

cs.CL2018

XNMT: The eXtensible Neural Machine Translation Toolkit

Graham Neubig, Matthias Sperber, Xinyi Wang +10

This paper describes XNMT, the eXtensible Neural Machine Translation toolkit. XNMT distin- guishes itself from other open-source NMT toolkits by its focus on modular code design, w…

cs.CL2018

Bayesian Models for Unit Discovery on a Very Low Resource Language

Lucas Ondel, Pierre Godard, Laurent Besacier +7

Developing speech technologies for low-resource languages has become a very active research field over the last decade. Among others, Bayesian models have shown some promising resu…

cs.CL2018

Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop

Odette Scharenborg, Laurent Besacier, Alan Black +16

We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and word…