3.5k citations · 5.4k across the 22 of their papers we have counts for
26 papers · 1 filter
Modular Speech-to-Text Translation for Zero-Shot Cross-Modal Transfer
Paul-Ambroise Duquenne, Holger Schwenk, Benoît Sagot
Recent research has shown that independently trained encoders and decoders, combined through a shared fixed-size representation, can achieve competitive performance in speech-to-te…
SONAR: Sentence-Level Multimodal and Language-Agnostic Representations
Paul-Ambroise Duquenne, Holger Schwenk, Benoît Sagot
We introduce SONAR, a new multilingual and multimodal fixed-size sentence embedding space. Our single text encoder, covering 200 languages, substantially outperforms existing sente…
SeamlessM4T: Massively Multilingual & Multimodal Machine Translation
Seamless Communication, Loïc Barrault, Yu-An Chung +65
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed…
xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages
Mingda Chen, Kevin Heffernan, Onur Çelebi +2
We introduce a new proxy score for evaluating bitext mining based on similarity in a multilingual embedding space: xSIM++. In comparison to xSIM, this improved proxy leverages rule…
BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric
Mingda Chen, Paul-Ambroise Duquenne, Pierre Andrews +4
End-to-End speech-to-speech translation (S2ST) is generally evaluated with text-based metrics. This means that generated speech has to be automatically transcribed, making the eval…
Speech-to-Speech Translation For A Real-world Unwritten Language
Peng-Jen Chen, Kevin Tran, Yilin Yang +13
We study speech-to-speech translation (S2ST) that translates speech from one language into another language and focuses on building systems to support languages without standard te…
SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations
Paul-Ambroise Duquenne, Hongyu Gong, Ning Dong +7
We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments…
Multilingual Representation Distillation with Contrastive Learning
Weiting Tan, Kevin Heffernan, Holger Schwenk +1
Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and…
No Language Left Behind: Scaling Human-Centered Machine Translation
NLLB Team, Marta R. Costa-jussà, James Cross +36
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,…
Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
Kevin Heffernan, Onur Çelebi, Holger Schwenk
Scaling multilingual representation learning beyond the hundred most frequent languages is challenging, in particular to cover the long tail of low-resource languages. A promising…
T-Modules: Translation Modules for Zero-Shot Cross-Modal Machine Translation
Paul-Ambroise Duquenne, Hongyu Gong, Benoît Sagot +1
We present a new approach to perform zero-shot cross-modal transfer between speech and text for translation tasks. Multilingual speech and text are encoded in a joint fixed-size re…
Beyond English-Centric Multilingual Machine Translation
Angela Fan, Shruti Bhosale, Holger Schwenk +14
Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. Ho…
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
Holger Schwenk, Guillaume Wenzek, Sergey Edunov +2
We show that margin-based bitext mining in a multilingual sentence space can be applied to monolingual corpora of billions of sentences. We are using ten snapshots of a curated com…
MLQA: Evaluating Cross-lingual Extractive Question Answering
Patrick Lewis, Barlas Oğuz, Ruty Rinott +2
Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to…
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia
Holger Schwenk, Vishrav Chaudhary, Shuo Sun +2
We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 85 languages, including sever…
Low-Resource Corpus Filtering using Multilingual Sentence Embeddings
Vishrav Chaudhary, Yuqing Tang, Francisco Guzmán +2
In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEnten…
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
Mikel Artetxe, Holger Schwenk
We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different families and written in 28 different scripts…
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
Mikel Artetxe, Holger Schwenk
Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In…
XNLI: Evaluating Cross-lingual Sentence Representations
Alexis Conneau, Guillaume Lample, Ruty Rinott +4
State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a si…
Filtering and Mining Parallel Data in a Joint Multilingual Space
Holger Schwenk
We learn a joint multilingual sentence embedding and use the distance between sentences in different languages to filter noisy parallel data and to mine for parallel data in large…
A Corpus for Multilingual Document Classification in Eight Languages
Holger Schwenk, Xian Li
Cross-lingual document classification aims at training a document classifier on resources in one language and transferring it to a different language without any additional resourc…
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Alexis Conneau, Douwe Kiela, Holger Schwenk +2
Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of…
Learning Joint Multilingual Sentence Representations with Neural Machine Translation
Holger Schwenk, Matthijs Douze
In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation whic…
Very Deep Convolutional Networks for Text Classification
Alexis Conneau, Holger Schwenk, Loïc Barrault +1
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 c…