Rémi Philippe Lebret

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Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...

We propose an automated image selection system to assist photo editors in selecting suitable images for news articles. The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs. It is equipped with char-level wo ...

In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space ...

We propose a novel fully-automated approach towards inducing multilingual taxonomies fromWikipedia. Given an English taxonomy, our approach first leverages the interlanguage links of Wikipedia to automatically construct training datasets for the is-a relat ...

Online debates provide critical insights into public opinion and societal trends, yet the unstructured nature of these discussions presents significant challenges for analysis. In this paper, we present SAGESSE, a novel argumentation parsing pipeline tailo ...

Lots of machine learning models with applications in Multimedia Search are released as Open Source Software. However, integrating these models into an application is not always an easy task due to the lack of a consistent interface to run, train or distrib ...

Privacy policies are the primary channel through which companies inform users about their data collection and sharing practices. These policies are often long and difficult to comprehend. Short notices based on information extracted from privacy policies h ...

Recent transformer language models achieve outstanding results in many natural language processing (NLP) tasks. However, their enormous size often makes them impractical on memory-constrained devices, requiring practitioners to compress them to smaller net ...

Vision-Language Pre-training (VLP) has advanced the performance of many visionlanguage tasks, such as image-text retrieval, visual entailment, and visual reasoning. The pre-training mostly utilizes lexical databases and image queries in English. Previous w ...

The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters. However, due to deployment constraints in edge devices, there has been a rising interest in the compression of thes ...

Multilingual pre-trained language models transfer remarkably well on cross-lingual downstream tasks. However, the extent to which they learn language-neutral representations (i.e., shared representations that encode similar phenomena across languages), and ...

Rémi Philippe Lebret

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Word Embeddings for Natural Language Processing

Upgrading the Newsroom: An Automated Image Selection System for News Articles

Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task

280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia Using Character-Level Classification

SAGESSE: A System for Argument Generation, Extraction and Structuring of Social Exchanges

MoZuMa: A Model Zoo for Multimedia Applications

Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning

Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models

Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages

Direction is what you need: Improving Word Embedding Compression in Large Language Models

Discovering Language-neutral Sub-networks in Multilingual Language Models