QuickEdit: Editing Text & Translations via Simple Delete Actions
By David Grangier, Michael Auli. 2017
10 citations recorded by the source corpus.
We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing and relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change. Our model then generates a new sentence which reformulates the initial sentence by avoiding the words from the marked tokens. Our approach builds upon neural sequence-to-sequence modeling and introduces a neural network which takes as input a sentence along with deleted token markers. Our model is trained on translation bi-text by simulating post-edits. Our results on post-editing for machine translation and paraphrasing evaluate the performance of our approach. We show +11.4 BLEU with limited post-editing effort on the WMT-14 English-German translation task (25.2 to 36.6), which represents +5.9 BLEU over the post-editing baseline (30.7 to 36.6).
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jason Aaron Edward Weston
- Kyunghyun Cho
- Emily Dinan
- Mohit Bansal
- Jianfeng Gao
- Luke S. Zettlemoyer
- Douwe Kiela
- Yoshua Bengio
In Natural Language Processing
- Jason Aaron Edward Weston
- Kyunghyun Cho
- Emily Dinan
- Mohit Bansal
- Douwe Kiela
- Jianfeng Gao
- Alexander H. Miller
- Y-Lan Boureau
Citations in the corpus
- Retrieve and Refine: Improved Sequence Generation Models For Dialogue (2018, background): “initial predictions (Junczys-Dowmunt and Grundkiewicz, 2017; Niehues et al., 2016; Novak et al., 2016; Xia et al., 2017; Grangier and Auli, 2017) or combining with retrieval (Gu et al.”
- Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (2018, background): “QuickEdit (Grangier and Auli, 2017) and deliberation network (Xia et al.”
- Learning from Chunk-based Feedback in Neural Machine Translation (2018, background): “Grangier and Auli ( 2017) have studied the task of paraphrasing an already generated translation by excluding words that the user has marked as incorrect.”