Search Results for author: Didier Schwab

Found 49 papers, 14 papers with code

Visualizing Cross‐Lingual Discourse Relations in Multilingual TED Corpora

1 code implementation CODI 2021 Zae Myung Kim, Vassilina Nikoulina, Dongyeop Kang, Didier Schwab, Laurent Besacier

This paper presents an interactive data dashboard that provides users with an overview of the preservation of discourse relations among 28 language pairs.


WordNet and beyond: the case of lexical access

no code implementations GWC 2016 Michael Zock, Didier Schwab

Next we will show under what conditions WN is suitable for word access, and finally we will present a roadmap showing the obstacles to be overcome to build a resource allowing the text producer to find the word s/he is looking for.


UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition

no code implementations20 Jul 2023 Aidan Mannion, Thierry Chevalier, Didier Schwab, Lorraine Geouriot

In the biomedical domain, significant progress has been made in adapting this paradigm to NLP tasks that require the integration of domain-specific knowledge as well as statistical modelling of language.

Document Classification named-entity-recognition +4

Pre-training for Speech Translation: CTC Meets Optimal Transport

1 code implementation27 Jan 2023 Phuong-Hang Le, Hongyu Gong, Changhan Wang, Juan Pino, Benjamin Lecouteux, Didier Schwab

Nevertheless, CTC is only a partial solution and thus, in our second contribution, we propose a novel pre-training method combining CTC and optimal transport to further reduce this gap.

Multi-Task Learning Speech-to-Text Translation +1

Effect Of Personalized Calibration On Gaze Estimation Using Deep-Learning

no code implementations27 Sep 2021 Nairit Bandyopadhyay, Sébastien Riou, Didier Schwab

We trained a multi modal convolutional neural network and analysed its performance with and without calibration and this evaluation provides clear insights on how calibration improved the performance of the Deep Learning model in estimating gaze in the wild.

Gaze Estimation

Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads?

no code implementations Findings (ACL) 2021 Zae Myung Kim, Laurent Besacier, Vassilina Nikoulina, Didier Schwab

Recent studies on the analysis of the multilingual representations focus on identifying whether there is an emergence of language-independent representations, or whether a multilingual model partitions its weights among different languages.

Machine Translation NMT +1

Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation

1 code implementation COLING 2020 Hang Le, Juan Pino, Changhan Wang, Jiatao Gu, Didier Schwab, Laurent Besacier

We propose two variants of these architectures corresponding to two different levels of dependencies between the decoders, called the parallel and cross dual-decoder Transformers, respectively.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset

1 code implementation LREC 2020 Jibril Frej, Didier Schwab, Jean-Pierre Chevallet

Since most standard ad-hoc information retrieval datasets publicly available for academic research (e. g. Robust04, ClueWeb09) have at most 250 annotated queries, the recent deep learning models for information retrieval perform poorly on these datasets.

Ad-Hoc Information Retrieval Information Retrieval +1

The LIG system for the English-Czech Text Translation Task of IWSLT 2019

no code implementations EMNLP (IWSLT) 2019 Loïc Vial, Benjamin Lecouteux, Didier Schwab, Hang Le, Laurent Besacier

Therefore, we implemented a Transformer-based encoder-decoder neural system which is able to use the output of a pre-trained language model as input embeddings, and we compared its performance under three configurations: 1) without any pre-trained language model (constrained), 2) using a language model trained on the monolingual parts of the allowed English-Czech data (constrained), and 3) using a language model trained on a large quantity of external monolingual data (unconstrained).

Language Modelling Machine Translation +1

ArbEngVec : Arabic-English Cross-Lingual Word Embedding Model

no code implementations WS 2019 Raki Lachraf, El Moatez Billah Nagoudi, Youcef Ayachi, Ahmed Abdelali, Didier Schwab

Word Embeddings (WE) are getting increasingly popular and widely applied in many Natural Language Processing (NLP) applications due to their effectiveness in capturing semantic properties of words; Machine Translation (MT), Information Retrieval (IR) and Information Extraction (IE) are among such areas.

Information Retrieval Machine Translation +6

Sense Vocabulary Compression through the Semantic Knowledge of WordNet for Neural Word Sense Disambiguation

2 code implementations GWC 2019 Loïc Vial, Benjamin Lecouteux, Didier Schwab

In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database.

Word Sense Disambiguation

Improving the Coverage and the Generalization Ability of Neural Word Sense Disambiguation through Hypernymy and Hyponymy Relationships

no code implementations2 Nov 2018 Loïc Vial, Benjamin Lecouteux, Didier Schwab

Our method leads to state of the art results on most WSD evaluation tasks, while improving the coverage of supervised systems, reducing the training time and the size of the models, without additional training data.

Word Sense Disambiguation

Système de traduction automatique statistique Anglais-Arabe

no code implementations6 Feb 2018 Marwa Hadj Salah, Didier Schwab, Hervé Blanchon, Mounir Zrigui

Machine translation (MT) is the process of translating text written in a source language into text in a target language.

Machine Translation Translation

Uniformisation de corpus anglais annot\'es en sens (Unification of sense annotated English corpora for word sense disambiguation)

no code implementations JEPTALNRECITAL 2017 Lo{\"\i}c Vial, Benjamin Lecouteux, Didier Schwab

Pour la d{\'e}sambigu{\"\i}sation lexicale en anglais, on compte aujourd{'}hui une quinzaine de corpus annot{\'e}s en sens dans des formats souvent diff{\'e}rents et provenant de diff{\'e}rentes versions du Princeton WordNet.

Word Sense Disambiguation

Deep Investigation of Cross-Language Plagiarism Detection Methods

1 code implementation WS 2017 Jeremy Ferrero, Laurent Besacier, Didier Schwab, Frederic Agnes

This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts).

Comparison of Global Algorithms in Word Sense Disambiguation

no code implementations7 Apr 2017 Loïc Vial, Andon Tchechmedjiev, Didier Schwab

We find that CSA, GA and SA all eventually converge to similar results (0. 98 F1 score), but CSA gets there faster (in fewer scorer calls) and reaches up to 0. 95 F1 before SA in fewer scorer calls.

Word Sense Disambiguation

Semantic Similarity of Arabic Sentences with Word Embeddings

no code implementations WS 2017 El Moatez Billah Nagoudi, Didier Schwab

Semantic textual similarity is the basis of countless applications and plays an important role in diverse areas, such as information retrieval, plagiarism detection, information extraction and machine translation.

Descriptive Information Retrieval +10

Am\'elioration de la traduction automatique d'un corpus annot\'e (Improvement of the automatic translation of an annotated corpus)

no code implementations JEPTALNRECITAL 2016 Marwa Hadj Salah, Herv{\'e} Blanchon, Mounir Zrigui, Didier Schwab

Dans cet article, nous pr{\'e}sentons une m{\'e}thode pour am{\'e}liorer la traduction automatique d{'}un corpus annot{\'e} et porter ses annotations de l{'}anglais vers une langue cible.

Extension lexicale de d\'efinitions gr\^ace \`a des corpus annot\'es en sens (Lexical Expansion of definitions based on sense-annotated corpus )

no code implementations JEPTALNRECITAL 2016 Lo{\"\i}c Vial, Andon Tchechmedjiev, Didier Schwab

La proximit{\'e} s{\'e}mantique de deux d{\'e}finitions est {\'e}valu{\'e}e en comptant le nombre de mots communs dans les d{\'e}finitions correspondantes dans un dictionnaire.

Cr\'eation rapide et efficace d'un syst\`eme de d\'esambigu\"\isation lexicale pour une langue peu dot\'ee

no code implementations JEPTALNRECITAL 2015 Mohammad Nasiruddin, Andon Tchechmedjiev, Herv{\'e} Blanchon, Didier Schwab

Nous pr{\'e}sentons une m{\'e}thode pour cr{\'e}er rapidement un syst{\`e}me de d{\'e}sambigu{\"\i}sation lexicale (DL) pour une langue L peu dot{\'e}e pourvu que l{'}on dispose d{'}un syst{\`e}me de traduction automatique statistique (TAS) d{'}une langue riche en corpus annot{\'e}s en sens (ici l{'}anglais) vers L. Il est, en effet, plus facile de disposer des ressources n{\'e}cessaires {\`a} la cr{\'e}ation d{'}un syst{\`e}me de TAS que des ressources d{\'e}di{\'e}es n{\'e}cessaires {\`a} la cr{\'e}ation d{'}un syst{\`e}me de DL pour la langue L. Notre m{\'e}thode consiste {\`a} traduire automatiquement un corpus annot{\'e} en sens vers la langue L, puis de cr{\'e}er le syst{\`e}me de d{\'e}sambigu{\"\i}sation pour L par des m{\'e}thodes supervis{\'e}es classiques.

Cannot find the paper you are looking for? You can Submit a new open access paper.