| Convolutional sequence to sequence learning J Gehring, M Auli, D Grangier, D Yarats, YN Dauphin arXiv preprint arXiv:1705.03122, 2017 | 1545 | 2017 |
| Sequence level training with recurrent neural networks MA Ranzato, S Chopra, M Auli, W Zaremba arXiv preprint arXiv:1511.06732, 2015 | 823 | 2015 |
| Language modeling with gated convolutional networks YN Dauphin, A Fan, M Auli, D Grangier International conference on machine learning, 933-941, 2017 | 742 | 2017 |
| A neural network approach to context-sensitive generation of conversational responses A Sordoni, M Galley, M Auli, C Brockett, Y Ji, M Mitchell, JY Nie, J Gao, ... arXiv preprint arXiv:1506.06714, 2015 | 666 | 2015 |
| Abstractive sentence summarization with attentive recurrent neural networks S Chopra, M Auli, AM Rush Proceedings of the 2016 Conference of the North American Chapter of the …, 2016 | 524 | 2016 |
| fairseq: A fast, extensible toolkit for sequence modeling M Ott, S Edunov, A Baevski, A Fan, S Gross, N Ng, D Grangier, M Auli arXiv preprint arXiv:1904.01038, 2019 | 264 | 2019 |
| Joint language and translation modeling with recurrent neural networks M Auli, M Galley, C Quirk, G Zweig | 259 | 2013 |
| A convolutional encoder model for neural machine translation J Gehring, M Auli, D Grangier, YN Dauphin arXiv preprint arXiv:1611.02344, 2016 | 230 | 2016 |
| Understanding back-translation at scale S Edunov, M Ott, M Auli, D Grangier arXiv preprint arXiv:1808.09381, 2018 | 225 | 2018 |
| Scaling neural machine translation M Ott, S Edunov, D Grangier, M Auli arXiv preprint arXiv:1806.00187, 2018 | 177 | 2018 |
| Neural text generation from structured data with application to the biography domain R Lebret, D Grangier, M Auli arXiv preprint arXiv:1603.07771, 2016 | 168 | 2016 |
| Pay less attention with lightweight and dynamic convolutions F Wu, A Fan, A Baevski, YN Dauphin, M Auli arXiv preprint arXiv:1901.10430, 2019 | 128 | 2019 |
| deltableu: A discriminative metric for generation tasks with intrinsically diverse targets M Galley, C Brockett, A Sordoni, Y Ji, M Auli, C Quirk, M Mitchell, J Gao, ... arXiv preprint arXiv:1506.06863, 2015 | 120 | 2015 |
| Strategies for training large vocabulary neural language models W Chen, D Grangier, M Auli arXiv preprint arXiv:1512.04906, 2015 | 118 | 2015 |
| Wizard of wikipedia: Knowledge-powered conversational agents E Dinan, S Roller, K Shuster, A Fan, M Auli, J Weston arXiv preprint arXiv:1811.01241, 2018 | 100 | 2018 |
| Controllable abstractive summarization A Fan, D Grangier, M Auli arXiv preprint arXiv:1711.05217, 2017 | 92 | 2017 |
| 3d human pose estimation in video with temporal convolutions and semi-supervised training D Pavllo, C Feichtenhofer, D Grangier, M Auli Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 89 | 2019 |
| Classical structured prediction losses for sequence to sequence learning S Edunov, M Ott, M Auli, D Grangier, MA Ranzato arXiv preprint arXiv:1711.04956, 2017 | 87 | 2017 |
| Adaptive input representations for neural language modeling A Baevski, M Auli arXiv preprint arXiv:1809.10853, 2018 | 79 | 2018 |
| Analyzing uncertainty in neural machine translation M Ott, M Auli, D Grangier, MA Ranzato arXiv preprint arXiv:1803.00047, 2018 | 60 | 2018 |
