# A Template-based Method for Constrained Neural Machine Translation

arXiv: https://arxiv.org/abs/2205.11255 · HTML: https://mlsanity.com/paper/2205.11255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search](https://mlsanity.com/paper/1704.07138/) (2017)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://mlsanity.com/paper/1901.10430/) (2019)
- [Levenshtein Transformer](https://mlsanity.com/paper/1905.11006/) (2019)
- [Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation](https://mlsanity.com/paper/1804.06609/) (2018)
- [Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation](https://mlsanity.com/paper/1904.03396/) (2019)
- [Training Neural Machine Translation To Apply Terminology Constraints](https://mlsanity.com/paper/1906.01105/) (2019)
- [Domain Robustness in Neural Machine Translation](https://mlsanity.com/paper/1911.03109/) (2019)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [Lexically Constrained Neural Machine Translation with Levenshtein Transformer](https://mlsanity.com/paper/2004.12681/) (2020)
- [Neural Machine Translation Decoding with Terminology Constraints](https://mlsanity.com/paper/1805.03750/) (2018)
- [End-to-End Neural Word Alignment Outperforms GIZA++](https://mlsanity.com/paper/2004.14675/) (2020)
- [SYSTRAN's Pure Neural Machine Translation Systems](https://mlsanity.com/paper/1610.05540/) (2016)
- [Generative Adversarial Network for Abstractive Text Summarization](https://mlsanity.com/paper/1711.09357/) (2017)
- [Facilitating Terminology Translation with Target Lemma Annotations](https://mlsanity.com/paper/2101.10035/) (2021)
- [On the Evaluation of Machine Translation for Terminology Consistency](https://mlsanity.com/paper/2106.11891/) (2021)
- [Neural Machine Translation with Explicit Phrase Alignment](https://mlsanity.com/paper/1911.11520/) (2019)
- [Continuous Learning in Neural Machine Translation using Bilingual Dictionaries](https://mlsanity.com/paper/2102.06558/) (2021)
- [A High-Quality Multilingual Dataset for Structured Documentation Translation](https://mlsanity.com/paper/2006.13425/) (2020)
- [Integrating Vectorized Lexical Constraints for Neural Machine Translation](https://mlsanity.com/paper/2203.12210/) (2022)
- [Neural Machine Translation with Noisy Lexical Constraints](https://mlsanity.com/paper/1908.04664/) (2019)
- [End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages](https://mlsanity.com/paper/2106.12398/) (2021)

## Abstract (arXiv)

Machine translation systems are expected to cope with various types of constraints in many practical scenarios. While neural machine translation (NMT) has achieved strong performance in unconstrained cases, it is non-trivial to impose pre-specified constraints into the translation process of NMT models. Although many approaches have been proposed to address this issue, most existing methods can not satisfy the following three desiderata at the same time: (1) high translation quality, (2) high match accuracy, and (3) low latency. In this work, we propose a template-based method that can yield results with high translation quality and match accuracy and the inference speed of our method is comparable with unconstrained NMT models. Our basic idea is to rearrange the generation of constrained and unconstrained tokens through a template. Our method does not require any changes in the model architecture and the decoding algorithm. Experimental results show that the proposed template-based approach can outperform several representative baselines in both lexically and structurally constrained translation tasks.