EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints

By Weijia Xu, Marine Carpuat. 2020

52 citations recorded by the source corpus.

Abstract We introduce an Edit-Based TransfOrmer with Repositioning (EDITOR), which makes sequence generation flexible by seamlessly allowing users to specify preferences in output lexical choice. Building on recent models for non-autoregressive sequence generation (Gu et al., 2019), EDITOR generates new sequences by iteratively editing hypotheses. It relies on a novel reposition operation designed to disentangle lexical choice from word positioning decisions, while enabling efficient oracles for imitation learning and parallel edits at decoding time. Empirically, EDITOR uses soft lexical constraints more effectively than the Levenshtein Transformer (Gu et al., 2019) while speeding up decoding dramatically compared to constrained beam search (Post and Vilar, 2018). EDITOR also achieves comparable or better translation quality with faster decoding speed than the Levenshtein Transformer on standard Romanian-English, English-German, and English-Japanese machine translation tasks.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 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

1. Pascale Fung
2. Andrea Madotto
3. Wenliang Dai
4. Nayeon Lee
5. Yan Xu
6. Etsuko Ishii
7. Ziwei Ji
8. Dan Su

In Computer Vision and Pattern Recognition

1. Pascale Fung
2. Wenliang Dai
3. Ziwei Ji
4. Dan Su
5. Weijia Xu
6. Marine Carpuat
7. Tiezheng Yu
8. Yejin Bang

In Natural Language Processing

1. Pascale Fung
2. Nayeon Lee
3. Andrea Madotto
4. Etsuko Ishii
5. Yejin Bang
6. Marine Carpuat
7. Weijia Xu
8. Yan Xu

Citations in the corpus

• Survey of Hallucination in Natural Language Generation (2022, methodology): “[33, 70, 146, 171, 177, 214] and [213] use constrained decoding to incorporate specific terminology into MT, but the above methods can be repurposed to mitigate hallucinations.”
• Rule-based Morphological Inflection Improves Neural Terminology Translation (2021, methodology): “• NAR with constraints (NAR+C) that integrates constraints as the initial sequence in EDITOR without explicit inflection.”