Search Results for author: François Yvon

Found 29 papers, 8 papers with code

Ré-ordonnancement via programmation dynamique pour l’adaptation cross-lingue d’un analyseur en dépendances (Sentence reordering via dynamic programming for cross-lingual dependency parsing )

no code implementations JEP/TALN/RECITAL 2022 Nicolas Devatine, Caio Corro, François Yvon

Cet article s’intéresse au transfert cross-lingue d’analyseurs en dépendances et étudie des méthodes pour limiter l’effet potentiellement néfaste pour le transfert de divergences entre l’ordre des mots dans les langues source et cible.

Dependency Parsing

Multi-Domain Adaptation in Neural Machine Translation with Dynamic Sampling Strategies

no code implementations EAMT 2022 Minh-Quang Pham, Josep Crego, François Yvon

In this paper, we study dynamic data selection strategies that are able to automatically re-evaluate the usefulness of data samples and to evolve a data selection policy in the course of training.

Machine Translation Translation +1

Weakly Supervised Word Segmentation for Computational Language Documentation

1 code implementation ACL 2022 Shu Okabe, Laurent Besacier, François Yvon

Word and morpheme segmentation are fundamental steps of language documentation as they allow to discover lexical units in a language for which the lexicon is unknown.

Incremental Learning

Two-Step MT: Predicting Target Morphology

no code implementations IWSLT 2016 Franck Burlot, Elena Knyazeva, Thomas Lavergne, François Yvon

This paper describes a two-step machine translation system that addresses the issue of translating into a morphologically rich language (English to Czech), by performing separately the translation and the generation of target morphology.

Machine Translation Translation

Priming Neural Machine Translation

no code implementations WMT (EMNLP) 2020 Minh Quang Pham, Jitao Xu, Josep Crego, François Yvon, Jean Senellart

Priming is a well known and studied psychology phenomenon based on the prior presentation of one stimulus (cue) to influence the processing of a response.

Machine Translation Translation

Optimizing Word Alignments with Better Subword Tokenization

no code implementations MTSummit 2021 Anh Khoa Ngo Ho, François Yvon

Word alignment identify translational correspondences between words in a parallel sentence pair and are used and for example and to train statistical machine translation and learn bilingual dictionaries or to perform quality estimation.

Machine Translation Translation +1

Vers la production automatique de sous-titres adaptés à l’affichage (Towards automatic adapted monolingual captioning)

no code implementations JEP/TALN/RECITAL 2021 François Buet, François Yvon

Une façon de réaliser un sous-titrage automatique monolingue est d’associer un système de reconnaissance de parole avec un modèle de traduction de la transcription vers les sous-titres.

Biais de genre dans un système de traduction automatiqueneuronale : une étude préliminaire (Gender Bias in Neural Translation : a preliminary study )

no code implementations JEP/TALN/RECITAL 2021 Guillaume Wisniewski, Lichao Zhou, Nicolas Ballier, François Yvon

Cet article présente les premiers résultats d’une étude en cours sur les biais de genre dans les corpus d’entraînements et dans les systèmes de traduction neuronale.

Evaluating Subtitle Segmentation for End-to-end Generation Systems

1 code implementation19 May 2022 Alina Karakanta, François Buet, Mauro Cettolo, François Yvon

Subtitle segmentation can be evaluated with sequence segmentation metrics against a human reference.

Joint Generation of Captions and Subtitles with Dual Decoding

1 code implementation IWSLT (ACL) 2022 Jitao Xu, François Buet, Josep Crego, Elise Bertin-Lemée, François Yvon

As the amount of audio-visual content increases, the need to develop automatic captioning and subtitling solutions to match the expectations of a growing international audience appears as the only viable way to boost throughput and lower the related post-production costs.

Screening Gender Transfer in Neural Machine Translation

no code implementations EMNLP (BlackboxNLP) 2021 Guillaume Wisniewski, Lichao Zhu, Nicolas Ballier, François Yvon

This paper aims at identifying the information flow in state-of-the-art machine translation systems, taking as example the transfer of gender when translating from French into English.

Machine Translation Translation

One Source, Two Targets: Challenges and Rewards of Dual Decoding

1 code implementation EMNLP 2021 Jitao Xu, François Yvon

Machine translation is generally understood as generating one target text from an input source document.

Machine Translation Translation

Graph Algorithms for Multiparallel Word Alignment

1 code implementation EMNLP 2021 Ayyoob Imani, Masoud Jalili Sabet, Lütfi Kerem Şenel, Philipp Dufter, François Yvon, Hinrich Schütze

With the advent of end-to-end deep learning approaches in machine translation, interest in word alignments initially decreased; however, they have again become a focus of research more recently.

Link Prediction Machine Translation +3

Can You Traducir This? Machine Translation for Code-Switched Input

no code implementations NAACL (CALCS) 2021 Jitao Xu, François Yvon

Code-Switching (CSW) is a common phenomenon that occurs in multilingual geographic or social contexts, which raises challenging problems for natural language processing tools.

Machine Translation Natural Language Processing +1

Generative latent neural models for automatic word alignment

no code implementations AMTA 2020 Anh Khoa Ngo Ho, François Yvon

Word alignments identify translational correspondences between words in a parallel sentence pair and are used, for instance, to learn bilingual dictionaries, to train statistical machine translation systems or to perform quality estimation.

Machine Translation Natural Language Processing +3

Neural Baselines for Word Alignment

no code implementations EMNLP (IWSLT) 2019 Anh Khoa Ngo Ho, François Yvon

Word alignments identify translational correspondences between words in a parallel sentence pair and is used, for instance, to learn bilingual dictionaries, to train statistical machine translation systems , or to perform quality estimation.

Machine Translation Natural Language Processing +2

SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings

2 code implementations Findings of the Association for Computational Linguistics 2020 Masoud Jalili Sabet, Philipp Dufter, François Yvon, Hinrich Schütze

We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners, even with abundant parallel data; e. g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.

Machine Translation Multilingual Word Embeddings +2

Using Monolingual Data in Neural Machine Translation: a Systematic Study

1 code implementation WS 2018 Franck Burlot, François Yvon

Our findings confirm that back-translation is very effective and give new explanations as to why this is the case.

Machine Translation Translation

Unsupervised Word Segmentation from Speech with Attention

no code implementations18 Jun 2018 Pierre Godard, Marcely Zanon-Boito, Lucas Ondel, Alexandre Berard, François Yvon, Aline Villavicencio, Laurent Besacier

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, unwritten language (UL).

Acoustic Unit Discovery Machine Translation +1

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