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Found 26 papers, 5 papers with code
Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification
This study is part of the debate on the efficiency of large versus small language models for text classification by prompting. We assess the performance of small language models in zero-shot text classification, challenging the prevailing dominance of large models. Across 15 datasets, our investigation benchmarks language models from 77M to 40B parameters using different architectures and scoring functions.
A Benchmark Evaluation of Clinical Named Entity Recognition in French
Objective: This paper presentsan evaluation of masked language models for biomedical French on the task of clinical named entity recognition. Material and methods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare them tostandard French models CamemBERT, FlauBERT and FrALBERT as well as multilingual mBERT using three publicallyavailable corpora for clinical named entity recognition in French.
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark
A combination ofmultiple datasets, including the MEDIA dataset, was suggested for training this joint model.
mALBERT: Is a Compact Multilingual BERT Model Still Worth It?
Within the current trend of Pretained Language Models (PLM), emerge more and more criticisms about the ethical andecological impact of such models.
Benchmarking Transformers-based models on French Spoken Language Understanding tasks
In this paper, we propose a unified benchmark, focused on evaluating models quality and their ecological impact on two well-known French spoken language understanding tasks.
On the cross-lingual transferability of multilingual prototypical models across NLU tasks
Supervised deep learning-based approaches have been applied to task-oriented dialog and have proven to be effective for limited domain and language applications when a sufficient number of training examples are available.
On the Usability of Transformers-based models for a French Question-Answering task
In this paper, we establish a state-of-the-art of the efforts dedicated to the usability of Transformer-based models and propose to evaluate these improvements on the question-answering performances of French language which have few resources.
Neural Networks approaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation Task
In this paper, we present a study on a French Spoken Language Understanding (SLU) task: the MEDIA task.
Conception d'un syst\`eme de d\'etection d'intention pour un moteur de recherche sur Internet (Designing a User Intention Detection system for a Web Search Engine)
Cet article pr{\'e}sente notre {\'e}tude pour proposer un nouveau syst{\`e}me de d{\'e}tection d{'}intention pour le moteur de recherche sur Internet Qwant.
Using Whole Document Context in Neural Machine Translation
In Machine Translation, considering the document as a whole can help to resolve ambiguities and inconsistencies.
Qwant Research @DEFT 2019: Document matching and information retrieval using clinical cases
For this task, we propose an approach based on language models and evaluate the impact on the results of different preprocessings and matching techniques.
Qwant Research @DEFT 2019 : appariement de documents et extraction d'informations \`a partir de cas cliniques (Document matching and information retrieval using clinical cases)
Pour r{\'e}soudre cette t{\^a}che, nous proposons une approche reposant sur des mod{\`e}les de langue et {\'e}valuons l{'}impact de diff{\'e}rents pr{\'e}-traitements et de diff{\'e}rentes techniques d{'}appariement sur les r{\'e}sultats.
Image search using multilingual texts: a cross-modal learning approach between image and text
Moreover, by using multilingual embeddings we ensure that words from two different languages have close descriptors and thus are attached to similar images.
SYSTRAN Purely Neural MT Engines for WMT2017
This paper describes SYSTRAN's systems submitted to the WMT 2017 shared news translation task for English-German, in both translation directions.
Adaptation incr\'ementale de mod\`eles de traduction neuronaux (Incremental adaptation of neural machine translation models)
L{'}adaptation au domaine est un verrou scientifique en traduction automatique.
Conception d'une solution de d\'etection d'\'ev\'enements bas\'ee sur Twitter (Design of a solution for event detection from Tweeter)
Cet article pr{\'e}sente un syst{\`e}me d{'}alertes fond{\'e} sur la masse de donn{\'e}es issues de Tweeter.
Domain specialization: a post-training domain adaptation for Neural Machine Translation
Domain adaptation is a key feature in Machine Translation.
Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation
This paper proposes a first attempt to build an end-to-end speech-to-text translation system, which does not use source language transcription during learning or decoding.
SYSTRAN's Pure Neural Machine Translation Systems
Since the first online demonstration of Neural Machine Translation (NMT) by LISA, NMT development has recently moved from laboratory to production systems as demonstrated by several entities announcing roll-out of NMT engines to replace their existing technologies.
Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?
This paper presents an approach combining lexico-semantic resources and distributed representations of words applied to the evaluation in machine translation (MT).
Word2Vec vs DBnary ou comment (r\'e)concilier repr\'esentations distribu\'ees et r\'eseaux lexico-s\'emantiques ? Le cas de l'\'evaluation en traduction automatique (Word2Vec vs DBnary or how to bring back together vector representations and lexical resources ? A case study for machine translation evaluation)
Cet article pr{\'e}sente une approche associant r{\'e}seaux lexico-s{\'e}mantiques et repr{\'e}sentations distribu{\'e}es de mots appliqu{\'e}e {\`a} l{'}{\'e}valuation de la traduction automatique.
MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP
We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words).
Adaptation par enrichissement terminologique en traduction automatique statistique fond\'ee sur la g\'en\'eration et le filtrage de bi-segments virtuels
Nous pr{\'e}sentons des travaux pr{\'e}liminaires sur une approche permettant d{'}ajouter des termes bilingues {\`a} un syst{\`e}me de Traduction Automatique Statistique (TAS) {\`a} base de segments.
