Search Results for author: re

Found 92 papers, 6 papers with code

Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions

no code implementations ACL 2020 Tian Jin, Zhun Liu, Shengjia Yan, Alex Eichenberger, re, Louis-Philippe Morency

In this paper, we propose \textbf{N3} (\textbf{N}eural \textbf{N}etworks from \textbf{N}atural Language) - a new paradigm of synthesizing task-specific neural networks from language descriptions and a generic pre-trained model.

General Classification Image Classification +3

\'Etude des variations s\'emantiques \`a travers plusieurs dimensions (Studying semantic variations through several dimensions )

no code implementations JEPTALNRECITAL 2020 Syrielle Montariol, Alex Allauzen, re

Nous exp{\'e}rimentons sur un corpus de rapports financiers d{'}entreprises fran{\c{c}}aises, pour appr{\'e}hender les enjeux et pr{\'e}occupations propres {\`a} certaines p{\'e}riodes, acteurs et secteurs d{'}activit{\'e}s.

Ontology Matching Using Convolutional Neural Networks

no code implementations LREC 2020 Alex Bento, re, Amal Zouaq, Michel Gagnon

In order to achieve interoperability of information in the context of the Semantic Web, it is necessary to find effective ways to align different ontologies.

English WordNet 2020: Improving and Extending a WordNet for English using an Open-Source Methodology

no code implementations LREC 2020 John Philip McCrae, Alex Rademaker, re, Ewa Rudnicka, Francis Bond

WordNet, while one of the most widely used resources for NLP, has not been updated for a long time, and as such a new project English WordNet has arisen to continue the development of the model under an open-source paradigm.

Inclusion of Lithological terms (rocks and minerals) in The Open Wordnet for English

no code implementations LREC 2020 Alex Tessarollo, Alex Rademaker, re

We extend the Open WordNet for English (OWN-EN) with rock-related and other lithological terms using the authoritative source of GBA{'}s Thesaurus.

Inducing Document Structure for Aspect-based Summarization

1 code implementation ACL 2019 Lea Frermann, Alex Klementiev, re

In addition to improvements in summarization over topic-agnostic baselines, we demonstrate the benefit of the learnt document structure: we show that our models (a) learn to accurately segment documents by aspect; (b) can leverage the structure to produce both abstractive and extractive aspect-based summaries; and (c) that structure is particularly advantageous for summarizing long documents.

Abstractive Text Summarization

Interpr\'etation et visualisation contextuelle de NOTAMs (messages aux navigants a\'eriens) ()

no code implementations JEPTALNRECITAL 2019 Alex Arnold, re, G{\'e}rard Dupont, Catherine Kobus, Fran{\c{c}}ois Lancelot, Pooja Narayan

Dans cet article, nous pr{\'e}sentons une d{\'e}monstration de visualisation de l{'}information extraite automatiquement de la partie textuelle des NOTAMs.

Towards Incremental Learning of Word Embeddings Using Context Informativeness

1 code implementation ACL 2019 Alex Kabbach, re, Kristina Gulordava, Aur{\'e}lie Herbelot

In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way.

Incremental Learning Learning Word Embeddings

Butterfly Effects in Frame Semantic Parsing: impact of data processing on model ranking

1 code implementation COLING 2018 Alex Kabbach, re, Corentin Ribeyre, Aur{\'e}lie Herbelot

Knowing the state-of-the-art for a particular task is an essential component of any computational linguistics investigation.

Semantic Parsing

Learning with Noise-Contrastive Estimation: Easing training by learning to scale

no code implementations COLING 2018 Matthieu Labeau, Alex Allauzen, re

Noise-Contrastive Estimation (NCE) is a learning criterion that is regularly used to train neural language models in place of Maximum Likelihood Estimation, since it avoids the computational bottleneck caused by the output softmax.

Language Modelling Machine Translation +1

Algorithmes \`a base d'\'echantillonage pour l'entra\^\inement de mod\`eles de langue neuronaux (Here the title in English)

no code implementations JEPTALNRECITAL 2018 Matthieu Labeau, Alex Allauzen, re

L{'}estimation contrastive bruit{\'e}e (NCE) et l{'}{\'e}chantillonage par importance (IS) sont des proc{\'e}dures d{'}entra{\^\i}nement bas{\'e}es sur l{'}{\'e}chantillonage, que l{'}on utilise habituellement {\`a} la place de l{'}estimation du maximum de vraisemblance (MLE) pour {\'e}viter le calcul du softmax lorsque l{'}on entra{\^\i}ne des mod{\`e}les de langue neuronaux.

Topic Signatures in Political Campaign Speeches

no code implementations EMNLP 2017 Cl{\'e}ment Gautrais, Peggy Cellier, Ren{\'e} Quiniou, Alex Termier, re

Highlighting the recurrence of topics usage in candidates speeches is a key feature to identify the main ideas of each candidate during a political campaign.

Accurate Supervised and Semi-Supervised Machine Reading for Long Documents

no code implementations EMNLP 2017 Daniel Hewlett, Llion Jones, Alex Lacoste, re, Izzeddin Gur

We also evaluate the model in a semi-supervised setting by downsampling the WikiReading training set to create increasingly smaller amounts of supervision, while leaving the full unlabeled document corpus to train a sequence autoencoder on document windows.

Question Answering Reading Comprehension

Coarse-to-Fine Question Answering for Long Documents

no code implementations ACL 2017 Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alex Lacoste, re, Jonathan Berant

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.

Question Answering Reading Comprehension

Adaptation au domaine pour l'analyse morpho-syntaxique (Domain Adaptation for PoS tagging)

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.

Domain Adaptation POS

Repr\'esentations continues d\'eriv\'ees des caract\`eres pour un mod\`ele de langue neuronal \`a vocabulaire ouvert (Opening the vocabulary of neural language models with character-level word representations)

no code implementations JEPTALNRECITAL 2017 Matthieu Labeau, Alex Allauzen, re

Les repr{\'e}sentations continues des mots sont calcul{\'e}es {\`a} la vol{\'e}e {\`a} partir des caract{\`e}res les composant, gr{\`a}ce {\`a} une couche convolutionnelle suivie d{'}une couche de regroupement (pooling).

SENTER

An experimental analysis of Noise-Contrastive Estimation: the noise distribution matters

no code implementations EACL 2017 Matthieu Labeau, Alex Allauzen, re

Noise Contrastive Estimation (NCE) is a learning procedure that is regularly used to train neural language models, since it avoids the computational bottleneck caused by the output softmax.

Language Modelling Machine Translation +1

Valencer: an API to Query Valence Patterns in FrameNet

1 code implementation COLING 2016 Alex Kabbach, re, Corentin Ribeyre

This paper introduces Valencer: a RESTful API to search for annotated sentences matching a given combination of syntactic realizations of the arguments of a predicate {--} also called {`}valence pattern{'} {--} in the FrameNet database.

Question Answering

Suivi de contours d'articulateurs orofaciaux \`a partir d'IRM dynamique (Orofacial articulators tracking from dynamic MRI)

no code implementations JEPTALNRECITAL 2016 Mathieu Labrunie, Pierre Badin, Laurent Lamalle, Cori Vilain, re, Louis-Jean Bo{\"e}, Jens Frahm, Peter Birkholz

Nous pr{\'e}sentons une m{\'e}thode de pr{\'e}diction de contours m{\'e}diosagittaux des organes orofaciaux de la parole et la d{\'e}glutition {\`a} partir d{'}images IRM dynamiques.

NER

Une m\'ethode non-supervis\'ee pour la segmentation morphologique et l'apprentissage de morphotactique \`a l'aide de processus de Pitman-Yor (An unsupervised method for joint morphological segmentation and morphotactics learning using Pitman-Yor processes)

no code implementations JEPTALNRECITAL 2016 Kevin L{\"o}ser, Alex Allauzen, re

Cet article pr{\'e}sente un mod{\`e}le bay{\'e}sien non-param{\'e}trique pour la segmentation morphologique non supervis{\'e}e. Ce mod{\`e}le semi-markovien s{'}appuie sur des classes latentes de morph{\`e}mes afin de mod{\'e}liser les caract{\'e}ristiques morphotactiques du lexique, et son caract{\`e}re non-param{\'e}trique lui permet de s{'}adapter aux donn{\'e}es sans avoir {\`a} sp{\'e}cifier {\`a} l{'}avance l{'}inventaire des morph{\`e}mes ainsi que leurs classes.

Perception audio-visuelle de s\'equences VCV produites par des personnes porteuses de Trisomie 21 : une \'etude pr\'eliminaire (Auditory-visual Perception of VCVs Produced by People with Down Syndrome: a Preliminary Study)

no code implementations JEPTALNRECITAL 2016 Alex Hennequin, re, Am{\'e}lie Rochet-Capellan, Marion Dohen

Pour les deux locuteurs impliqu{\'e}s, l{'}intelligibilit{\'e} visuelle est n{\'e}anmoins {\'e}quivalente {\`a} celle des deux locuteurs ordinaires et compensent le d{\'e}ficit d{'}intelligibilit{\'e} auditive.

MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP

1 code implementation LREC 2016 Alex B{\'e}rard, re, Christophe Servan, Olivier Pietquin, Laurent Besacier

We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words).

Document Classification General Classification +2

Semantic Links for Portuguese

no code implementations LREC 2016 Fabricio Chalub, Livy Real, Alex Rademaker, re, Valeria de Paiva

This paper describes work on incorporating Princenton{'}s WordNet morphosemantics links to the fabric of the Portuguese OpenWordNet-PT.

Apprentissage discriminant des mod\`eles continus de traduction

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.

Grammaires phrastiques et discursives fond\'ees sur les TAG : une approche de D-STAG avec les ACG

no code implementations JEPTALNRECITAL 2015 Laurence Danlos, Aleks Maskharashvili, re, Sylvain Pogodalla

Cet encodage permet d{'}une part d{'}utiliser l{'}ordre sup{\'e}rieur pour l{'}interpr{\'e}tation s{\'e}mantique afin de construire des structures qui soient des DAG et non des arbres, et d{'}autre part d{'}utiliser les propri{\'e}t{\'e}s de composition d{'}ACG pour r{\'e}aliser naturellement l{'}interface entre grammaire phrastique et grammaire discursive.

Oublier ce qu'on sait, pour mieux apprendre ce qu'on ne sait pas : une \'etude sur les contraintes de type dans les mod\`eles CRF

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.

NomLex-PT: A Lexicon of Portuguese Nominalizations

no code implementations LREC 2014 Valeria de Paiva, Livy Real, Alex Rademaker, re, Gerard de Melo

This paper presents NomLex-PT, a lexical resource describing Portuguese nominalizations.

Representation of linguistic and domain knowledge for second language learning in virtual worlds

no code implementations LREC 2012 Alex Denis, re, Ingrid Falk, Claire Gardent, Laura Perez-Beltrachini

There has been much debate, both theoretical and practical, on how to link ontologies and lexicons in natural language processing (NLP) applications.

Text Generation

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