Loïc Barrault

Very Deep Convolutional Networks for Text Classification

2017

The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which have pushed the …

Findings of the First Shared Task on Lifelong Learning Machine Translation

2020

A lifelong learning system can adapt to new data without forgetting previously acquired knowledge. In this paper, we introduce the first benchmark for lifelong learning machine translation. For this purpose, we provide training, lifelong and …

Active correction for speaker diarization with human in the loop

2021

International audience

On Using Monolingual Corpora in Neural Machine Translation

2015 · HAL (Le Centre pour la Communication Scientifique Directe)

Recent work on end-to-end neural network-based architectures for machine translation has shown promising results for En-Fr and En-De translation. Arguably, one of the major factors behind this success has been the availability of high quality …

Very Deep Convolutional Networks for Natural Language Processing.

2016 · arXiv (Cornell University)

The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which are very successful …

What you can cram into a single vector: Probing sentence embeddings for\n linguistic properties

2018 · arXiv (Cornell University)

Although much effort has recently been devoted to training high-quality\nsentence embeddings, we still have a poor understanding of what they are\ncapturing. "Downstream" tasks, often based on sentence classification, are\ncommonly used to evaluate the quality of …

How2: A Large-scale Dataset for Multimodal Language Understanding

2018 · arXiv (Cornell University)

In this paper, we introduce How2, a multimodal collection of instructional videos with English subtitles and crowdsourced Portuguese translations. We also present integrated sequence-to-sequence baselines for machine translation, automatic speech recognition, spoken language translation, and …

Supervised Learning of Universal Sentence Representations from Natural\n Language Inference Data

2017 · arXiv (Cornell University)

Many modern NLP systems rely on word embeddings, previously trained in an\nunsupervised manner on large corpora, as base features. Efforts to obtain\nembeddings for larger chunks of text, such as sentences, have however not been\nso successful. …

What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties

2018

Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. "Downstream" tasks, often based on sentence classification, are commonly used to evaluate …

Findings of the 2019 Conference on Machine Translation (WMT19)

2019

Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias Müller, Santanu Pal, Matt Post, Marcos Zampieri. Proceedings of the Fourth …

The IWSLT 2019 Evaluation Campaign

2019 · Research Publications (Maastricht University)

The IWSLT 2019 evaluation campaign featured three tasks: speech translation of (i) TED talks and (ii) How2 instructional videos from English into German and Portuguese, and (iii) text translation of TED talks from English into …

No Language Left Behind: Scaling Human-Centered Machine Translation

2022 · arXiv (Cornell University)

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset …