# Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation

arXiv: https://arxiv.org/abs/2211.07093 · HTML: https://mlsanity.com/paper/2211.07093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Scaling Neural Machine Translation](https://mlsanity.com/paper/1806.00187/) (2018)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [Guided Open Vocabulary Image Captioning with Constrained Beam Search](https://mlsanity.com/paper/1612.00576/) (2016)
- [Insertion-based Decoding with automatically Inferred Generation Order](https://mlsanity.com/paper/1902.01370/) (2019)
- [Universal Sentence Representation Learning with Conditional Masked Language Model](https://mlsanity.com/paper/2012.14388/) (2020)
- [Automatic Post-Editing for Machine Translation](https://mlsanity.com/paper/1910.08592/) (2019)
- [WeTS: A Benchmark for Translation Suggestion](https://mlsanity.com/paper/2110.05151/) (2021)

## Built on by (most cited first, 2 of 2)

- [Adaptable Logical Control for Large Language Models](https://mlsanity.com/paper/2406.13892/) (2024)
- [Synslator: An Interactive Machine Translation Tool with Online Learning](https://mlsanity.com/paper/2310.05025/) (2023)

## Abstract (arXiv)

Machine translation technology has made great progress in recent years, but it cannot guarantee error free results. Human translators perform post editing on machine translations to correct errors in the scene of computer aided translation. In favor of expediting the post editing process, many works have investigated machine translation in interactive modes, in which machines can automatically refine the rest of translations constrained by human's edits. Translation Suggestion (TS), as an interactive mode to assist human translators, requires machines to generate alternatives for specific incorrect words or phrases selected by human translators. In this paper, we utilize the parameterized objective function of neural machine translation (NMT) and propose a novel constrained decoding algorithm, namely Prefix Suffix Guided Decoding (PSGD), to deal with the TS problem without additional training. Compared to the state of the art lexically constrained decoding method, PSGD improves translation quality by an average of 10.87 BLEU and 8.62 BLEU on the WeTS and the WMT 2022 Translation Suggestion datasets, respectively, and reduces decoding time overhead by an average of 63.4% tested on the WMT translation datasets. Furthermore, on both of the TS benchmark datasets, it is superior to other supervised learning systems trained with TS annotated data.