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Neural Architectures for Named Entity Recognition

• Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer
• HLT-NAACL
• 2016

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that… (More)

Playing FPS Games with Deep Reinforcement Learning

• Guillaume Lample, Devendra Singh Chaplot
• AAAI
• 2017

Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of… (More)

Word Translation Without Parallel Data

• Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer, Hervé Jégou
• ArXiv
• 2017

State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be… (More)

Massively Multilingual Word Embeddings

• Waleed Ammar, George Mulcaire, Yulia Tsvetkov, Guillaume Lample, Chris Dyer, Noah A. Smith
• ArXiv
• 2016

We introduce new methods for estimating and evaluating embeddings of words from dozens of languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use… (More)

Evaluation of Word Vector Representations by Subspace Alignment

• Yulia Tsvetkov, Manaal Faruqui, Wang Ling, Guillaume Lample, Chris Dyer
• EMNLP
• 2015

Unsupervisedly learned word vectors have proven to provide exceptionally effective features in many NLP tasks. Most common intrinsic evaluations of vector quality measure correlation with similarity… (More)

Fader Networks: Manipulating Images by Sliding Attributes

• Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato
• NIPS
• 2017

This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the… (More)

Unsupervised Machine Translation Using Monolingual Corpora Only

• Guillaume Lample, Ludovic Denoyer, Marc'Aurelio Ranzato
• ArXiv
• 2017

Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to… (More)

Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning

• Yulia Tsvetkov, Sunayana Sitaram, +6 authors Chris Dyer
• HLT-NAACL
• 2016

We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on… (More)

Arnold: An Autonomous Agent to Play FPS Games

• Devendra Singh Chaplot, Guillaume Lample
• AAAI
• 2017

Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of… (More)

Phrase-Based & Neural Unsupervised Machine Translation

• Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato
• ArXiv
• 2018

Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of bitexts, which hinders their… (More)

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