Pierre Godard


2019

pdf bib
Controlling Utterance Length in NMT-based Word Segmentation with Attention
Pierre Godard | Laurent Besacier | François Yvon
Proceedings of the 16th International Conference on Spoken Language Translation

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 segmentation algorithms exist in the literature, they are challenged in real-world CLD settings by the small amount of available data. A possible remedy is to take advantage of glosses or translation in a foreign, well- resourced, language, which often exist for such data. In this paper, we explore and compare ways to exploit neural machine translation models to perform unsupervised boundary detection with bilingual information, notably introducing a new loss function for jointly learning alignment and segmentation. We experiment with an actual under-resourced language, Mboshi, and show that these techniques can effectively control the output segmentation length.

2018

pdf bib
A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments
Pierre Godard | Gilles Adda | Martine Adda-Decker | Juan Benjumea | Laurent Besacier | Jamison Cooper-Leavitt | Guy-Noel Kouarata | Lori Lamel | Hélène Maynard | Markus Mueller | Annie Rialland | Sebastian Stueker | François Yvon | Marcely Zanon-Boito
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)
Annie Rialland | Martine Adda-Decker | Guy-Noël Kouarata | Gilles Adda | Laurent Besacier | Lori Lamel | Elodie Gauthier | Pierre Godard | Jamison Cooper-Leavitt
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
XNMT: The eXtensible Neural Machine Translation Toolkit
Graham Neubig | Matthias Sperber | Xinyi Wang | Matthieu Felix | Austin Matthews | Sarguna Padmanabhan | Ye Qi | Devendra Sachan | Philip Arthur | Pierre Godard | John Hewitt | Rachid Riad | Liming Wang
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

pdf bib
Adaptor Grammars for the Linguist: Word Segmentation Experiments for Very Low-Resource Languages
Pierre Godard | Laurent Besacier | François Yvon | Martine Adda-Decker | Gilles Adda | Hélène Maynard | Annie Rialland
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

Computational Language Documentation attempts to make the most recent research in speech and language technologies available to linguists working on language preservation and documentation. In this paper, we pursue two main goals along these lines. The first is to improve upon a strong baseline for the unsupervised word discovery task on two very low-resource Bantu languages, taking advantage of the expertise of linguists on these particular languages. The second consists in exploring the Adaptor Grammar framework as a decision and prediction tool for linguists studying a new language. We experiment 162 grammar configurations for each language and show that using Adaptor Grammars for word segmentation enables us to test hypotheses about a language. Specializing a generic grammar with language specific knowledge leads to great improvements for the word discovery task, ultimately achieving a leap of about 30% token F-score from the results of a strong baseline.