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- Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann Dauphin
- ICML
- 2017
The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on… (More)
- Yann Dauphin, Angela Fan, Michael Auli, David Grangier
- ICML
- 2017
The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper… (More)
- Samy Bengio, Jason Weston, David Grangier
- NIPS
- 2010
Multi-class classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can… (More)
- David Grangier, Samy Bengio
- IEEE Transactions on Pattern Analysis and Machine…
- 2008
This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure… (More)
- Bing Bai, Jason Weston, +5 authors Kilian Q. Weinberger
- Information Retrieval
- 2009
In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a… (More)
- Jonas Gehring, Michael Auli, David Grangier, Yann Dauphin
- ACL
- 2017
The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. In this paper we present a faster and simpler architecture based on a succession of… (More)
- Bing Bai, Jason Weston, Ronan Collobert, David Grangier
- ECIR
- 2009
In this article we propose Supervised Semantic Indexing (SSI), an algorithm that is trained on (query, document) pairs of text documents to predict the quality of their match. Like Latent Semantic… (More)
- Rémi Lebret, David Grangier, Michael Auli
- EMNLP
- 2016
This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. It generates biographical sentences from fact tables on a new dataset of biographies from… (More)
This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure… (More)
- Wenlin Chen, David Grangier, Michael Auli
- ACL
- 2016
Training neural network language models over large vocabularies is still computationally very costly compared to count-based models such as Kneser-Ney. At the same time, neural language models are… (More)