2.6k citations · 5k across the 50 of their papers we have counts for
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Language Modeling with Gated Convolutional Networks
Yann N. Dauphin, Angela Fan, Michael Auli +1
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 conte…
A Convolutional Encoder Model for Neural Machine Translation
Jonas Gehring, Michael Auli, David Grangier +1
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…
Iterative Refinement for Machine Translation
Roman Novak, Michael Auli, David Grangier
Existing machine translation decoding algorithms generate translations in a strictly monotonic fashion and never revisit previous decisions. As a result, earlier mistakes cannot be…
Vocabulary Selection Strategies for Neural Machine Translation
Gurvan L'Hostis, David Grangier, Michael Auli
Classical translation models constrain the space of possible outputs by selecting a subset of translation rules based on the input sentence. Recent work on improving the efficiency…
Efficient softmax approximation for GPUs
Edouard Grave, Armand Joulin, Moustapha Cissé +2
We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies. Our approach, called adaptive softmax, circumvents the li…
Interactive Semantic Featuring for Text Classification
Camille Jandot, Patrice Simard, Max Chickering +2
In text classification, dictionaries can be used to define human-comprehensible features. We propose an improvement to dictionary features called smoothed dictionary features. Thes…