RESEARCH AREA
Natural Language Processing
Our team advances the state of the art in natural language understanding and generation, and deploys these systems at scale to break down language barriers, enable people to understand and communicate with anyone, and to provide a safe experience—no matter what language they speak.
The opportunities and challenges of this work are immense. Billions of people use our services to connect and communicate in their preferred language, but many of these languages lack traditional NLP resources and our systems need to be robust to the informal tone, slang and typos often found in daily communication.
Our research spans multiple areas across NLP and machine learning, including deep learning/neural networks, machine translation, natural language understanding and generation, low-resource NLP, question answering, dialogue, and cross-lingual and cross-domain transfer learning.
Latest Publications
June 03, 2019
NLP
Pay less attention with Lightweight and Dynamic Convolutions
In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results.
Felix Wu, Angela Fan, Alexei Baevski, Yann Dauphin, Michael Auli
June 03, 2019
October 31, 2018
NLP
Phrase-Based & Neural Unsupervised Machine Translation (EMNLP 2018, best paper)
In this work, we propose two methods for training translation models using only large monolingual corpora in each language, achieving state of the art results for both high-resource and low-resource languages.
Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato
October 31, 2018
July 15, 2018
NLP
Hierarchical Neural Story Generation. (ACL 2018, best paper honorable mention)
We explore story generation: creative systems that can build coherent and fluent passages of text about a topic through hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text.
Angela Fan, Michael Lewis, Yann Dauphin
July 15, 2018
October 29, 2018
NLP
XNLI: Evaluating Cross-lingual Sentence Representations
We introduce a dataset, called XNLI, that will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task in 15 languages, including low-resource languages such as Swahili and Urdu.
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel R. Bowman, Holger Schwenk, Ves Stoyanov
October 29, 2018
