no code implementations • WS 2019 • Marc Benzahra, Fran{\c{c}}ois Yvon
This article studies the relationship between text readability indice and automatic machine understanding systems.
no code implementations • NAACL 2019 • Guillaume Wisniewski, Fran{\c{c}}ois Yvon
The performance of Part-of-Speech tagging varies significantly across the treebanks of the Universal Dependencies project.
no code implementations • WS 2018 • Franck Burlot, Yves Scherrer, Vinit Ravishankar, Ond{\v{r}}ej Bojar, Stig-Arne Gr{\"o}nroos, Maarit Koponen, Tommi Nieminen, Fran{\c{c}}ois Yvon
Progress in the quality of machine translation output calls for new automatic evaluation procedures and metrics.
1 code implementation • EMNLP 2018 • MinhQuang Pham, Josep Crego, Jean Senellart, Fran{\c{c}}ois Yvon
Corpus-based approaches to machine translation rely on the availability of clean parallel corpora.
no code implementations • COLING 2018 • Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Not all dependencies are equal when training a dependency parser: some are straightforward enough to be learned with only a sample of data, others embed more complexity.
no code implementations • NAACL 2018 • Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Because the most common transition systems are projective, training a transition-based dependency parser often implies to either ignore or rewrite the non-projective training examples, which has an adverse impact on accuracy.
no code implementations • NAACL 2018 • Guillaume Wisniewski, Oph{\'e}lie Lacroix, Fran{\c{c}}ois Yvon
This work introduces a new strategy to compare the numerous conventions that have been proposed over the years for expressing dependency structures and discover the one for which a parser will achieve the highest parsing performance.
no code implementations • JEPTALNRECITAL 2018 • Franck Burlot, Fran{\c{c}}ois Yvon
Le nouvel {\'e}tat de l{'}art en traduction automatique (TA) s{'}appuie sur des m{\'e}thodes neuronales, qui diff{\'e}rent profond{\'e}ment des m{\'e}thodes utilis{\'e}es ant{\'e}rieurement.
no code implementations • JEPTALNRECITAL 2018 • Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Ce travail montre que la d{\'e}gradation des performances souvent observ{\'e}e lors de l{'}application d{'}un analyseur morpho-syntaxique {\`a} des donn{\'e}es hors domaine r{\'e}sulte souvent d{'}incoh{\'e}rences entre les annotations des ensembles de test et d{'}apprentissage.
no code implementations • EMNLP 2017 • Thomas Lavergne, Fran{\c{c}}ois Yvon
The computational complexity of linear-chain Conditional Random Fields (CRFs) makes it difficult to deal with very large label sets and long range dependencies.
no code implementations • WS 2017 • Jan-Thorsten Peter, Hermann Ney, Ond{\v{r}}ej Bojar, Ngoc-Quan Pham, Jan Niehues, Alex Waibel, Franck Burlot, Fran{\c{c}}ois Yvon, M{\=a}rcis Pinnis, Valters {\v{S}}ics, Jasmijn Bastings, Miguel Rios, Wilker Aziz, Philip Williams, Fr{\'e}d{\'e}ric Blain, Lucia Specia
no code implementations • CONLL 2017 • Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
This paper describes LIMSI{'}s submission to the CoNLL 2017 UD Shared Task, which is focused on small treebanks, and how to improve low-resourced parsing only by ad hoc combination of multiple views and resources.
no code implementations • JEPTALNRECITAL 2017 • {\'E}l{\'e}onor Bartenlian, Margot Lacour, Matthieu Labeau, Alex Allauzen, re, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Ce travail cherche {\`a} comprendre pourquoi les performances d{'}un analyseur morpho-syntaxiques chutent fortement lorsque celui-ci est utilis{\'e} sur des donn{\'e}es hors domaine.
no code implementations • JEPTALNRECITAL 2017 • Franck Burlot, Fran{\c{c}}ois Yvon
Lorsqu{'}ils sont traduits depuis une langue {\`a} morphologie riche vers l{'}anglais, les mots-formes sources contiennent des marques d{'}informations grammaticales pouvant {\^e}tre jug{\'e}es redondantes par rapport {\`a} l{'}anglais, causant une variabilit{\'e} formelle qui nuit {\`a} l{'}estimation des mod{\`e}les probabilistes.
no code implementations • EACL 2017 • Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
This paper formalizes a sound extension of dynamic oracles to global training, in the frame of transition-based dependency parsers.
no code implementations • COLING 2016 • Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
This paper studies cross-lingual transfer for dependency parsing, focusing on very low-resource settings where delexicalized transfer is the only fully automatic option.
no code implementations • COLING 2016 • Julia Ive, Fran{\c{c}}ois Yvon
In this paper, we study ways to extend sentence compression in a bilingual context, where the goal is to obtain parallel compressions of parallel sentences.
no code implementations • WS 2016 • Jan-Thorsten Peter, Tamer Alkhouli, Hermann Ney, Matthias Huck, Fabienne Braune, Alex Fraser, er, Ale{\v{s}} Tamchyna, Ond{\v{r}}ej Bojar, Barry Haddow, Rico Sennrich, Fr{\'e}d{\'e}ric Blain, Lucia Specia, Jan Niehues, Alex Waibel, Alex Allauzen, re, Lauriane Aufrant, Franck Burlot, Elena Knyazeva, Thomas Lavergne, Fran{\c{c}}ois Yvon, M{\=a}rcis Pinnis, Stella Frank
Ranked #12 on Machine Translation on WMT2016 English-Romanian
no code implementations • JEPTALNRECITAL 2016 • Oph{\'e}lie Lacroix, Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Cet article pr{\'e}sente une m{\'e}thode simple de transfert cross-lingue de d{\'e}pendances.
no code implementations • JEPTALNRECITAL 2016 • Fran{\c{c}}ois Yvon, Yong Xu, Marianna Apidianaki, Cl{\'e}ment Pillias, Cubaud Pierre
Le travail qui a conduit {\`a} cette d{\'e}monstration combine des outils de traitement des langues multilingues, en particulier l{'}alignement automatique, avec des techniques de visualisation et d{'}interaction.
no code implementations • JEPTALNRECITAL 2016 • Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Dans cet article, nous proposons trois am{\'e}liorations simples pour l{'}apprentissage global d{'}analyseurs en d{\'e}pendances par transition de type A RC E AGER : un oracle non d{\'e}terministe, la reprise sur le m{\^e}me exemple apr{\`e}s une mise {\`a} jour et l{'}entra{\^\i}nement en configurations sous-optimales.
no code implementations • LREC 2016 • Yong Xu, Fran{\c{c}}ois Yvon
Resources for evaluating sentence-level and word-level alignment algorithms are unsatisfactory.
no code implementations • LREC 2016 • Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Because of the small size of Romanian corpora, the performance of a PoS tagger or a dependency parser trained with the standard supervised methods fall far short from the performance achieved in most languages.
no code implementations • JEPTALNRECITAL 2015 • Quoc-Khanh Do, Alex Allauzen, re, Fran{\c{c}}ois Yvon
Alors que les r{\'e}seaux neuronaux occupent une place de plus en plus importante dans le traitement automatique des langues, les m{\'e}thodes d{'}apprentissage actuelles utilisent pour la plupart des crit{\`e}res qui sont d{\'e}corr{\'e}l{\'e}s de l{'}application.
no code implementations • JEPTALNRECITAL 2015 • Elena Knyazeva, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Gr{\^a}ce au lien que nous faisons entre apprentissage structur{\'e} et apprentissage par renforcement, nous sommes en mesure de proposer une m{\'e}thode th{\'e}oriquement bien justifi{\'e}e pour apprendre des m{\'e}thodes d{'}inf{\'e}rence approch{\'e}e. Les exp{\'e}riences que nous r{\'e}alisons sur quatre t{\^a}ches de TAL valident l{'}approche propos{\'e}e.
no code implementations • JEPTALNRECITAL 2015 • Nicolas P{\'e}cheux, Alex Allauzen, re, Thomas Lavergne, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Quand on dispose de connaissances a priori sur les sorties possibles d{'}un probl{\`e}me d{'}{\'e}tiquetage, il semble souhaitable d{'}inclure cette information lors de l{'}apprentissage pour simplifier la t{\^a}che de mod{\'e}lisation et acc{\'e}l{\'e}rer les traitements.
no code implementations • JEPTALNRECITAL 2014 • Li Gong, Aur{\'e}lien Max, Fran{\c{c}}ois Yvon
no code implementations • JEPTALNRECITAL 2014 • Guillaume Wisniewski, Nicolas P{\'e}cheux, Elena Knyazeva, Alex Allauzen, re, Fran{\c{c}}ois Yvon
no code implementations • LREC 2014 • Guillaume Wisniewski, Natalie K{\"u}bler, Fran{\c{c}}ois Yvon
In this paper, we present a freely available corpus of automatic translations accompanied with post-edited versions, annotated with labels identifying the different kinds of errors made by the MT system.
no code implementations • LREC 2014 • Nicolas P{\'e}cheux, Alex Allauzen, er, Fran{\c{c}}ois Yvon
In Statistical Machine Translation (SMT), the constraints on word reorderings have a great impact on the set of potential translations that are explored.
no code implementations • JEPTALNRECITAL 2012 • Souhir Gahbiche-Braham, H{\'e}l{\`e}ne Bonneau-Maynard, Thomas Lavergne, Fran{\c{c}}ois Yvon
no code implementations • LREC 2012 • Souhir Gahbiche-Braham, H{\'e}l{\`e}ne Bonneau-Maynard, Thomas Lavergne, Fran{\c{c}}ois Yvon
Arabic is a morphologically rich language, and Arabic texts abound of complex word forms built by concatenation of multiple subparts, corresponding for instance to prepositions, articles, roots prefixes, or suffixes.