2.6k citations · 8.2k across the 62 of their papers we have counts for
4 papers · 1 filter
Controllable Abstractive Summarization
Angela Fan, David Grangier, Michael Auli
Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the docume…
Classical Structured Prediction Losses for Sequence to Sequence Learning
Sergey Edunov, Myle Ott, Michael Auli +2
There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper…
QuickEdit: Editing Text & Translations by Crossing Words Out
David Grangier, Michael Auli
We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal relies on very simple interactions: a human edit…
Convolutional Sequence to Sequence Learning
Jonas Gehring, Michael Auli, David Grangier +2
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 base…