# Monotonic Simultaneous Translation with Chunk-wise Reordering and Refinement

arXiv: https://arxiv.org/abs/2110.09646 · HTML: https://mlsanity.com/paper/2110.09646/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 55 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [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)
- [Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/1606.07947/) (2016)
- [Neural Text Generation with Unlikelihood Training](https://mlsanity.com/paper/1908.04319/) (2019)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Levenshtein Transformer](https://mlsanity.com/paper/1905.11006/) (2019)
- [Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement](https://mlsanity.com/paper/1802.06901/) (2018)
- [Monotonic Infinite Lookback Attention for Simultaneous Machine Translation](https://mlsanity.com/paper/1906.05218/) (2019)
- [Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade](https://mlsanity.com/paper/2012.15833/) (2020)
- [Learning to Translate in Real-time with Neural Machine Translation](https://mlsanity.com/paper/1610.00388/) (2016)
- [Monotonic Multihead Attention](https://mlsanity.com/paper/1909.12406/) (2019)
- [Can neural machine translation do simultaneous translation?](https://mlsanity.com/paper/1606.02012/) (2016)
- [MTNT: A Testbed for Machine Translation of Noisy Text](https://mlsanity.com/paper/1809.00388/) (2018)
- [Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation](https://mlsanity.com/paper/1806.03661/) (2018)
- [Simultaneous Translation Policies: From Fixed to Adaptive](https://mlsanity.com/paper/2004.13169/) (2020)
- [Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation](https://mlsanity.com/paper/1909.01559/) (2019)
- [Re-translation versus Streaming for Simultaneous Translation](https://mlsanity.com/paper/2004.03643/) (2020)
- [Future-Guided Incremental Transformer for Simultaneous Translation](https://mlsanity.com/paper/2012.12465/) (2020)
- [End-to-End Neural Word Alignment Outperforms GIZA++](https://mlsanity.com/paper/2004.14675/) (2020)
- [Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext](https://mlsanity.com/paper/1706.01847/) (2017)
- [Simultaneous Translation with Flexible Policy via Restricted Imitation Learning](https://mlsanity.com/paper/1906.01135/) (2019)
- [Simulated Multiple Reference Training Improves Low-Resource Machine Translation](https://mlsanity.com/paper/2004.14524/) (2020)
- [Simultaneous Machine Translation with Visual Context](https://mlsanity.com/paper/2009.07310/) (2020)
- [JESC: Japanese-English Subtitle Corpus](https://mlsanity.com/paper/1710.10639/) (2017)
- [Opportunistic Decoding with Timely Correction for Simultaneous Translation](https://mlsanity.com/paper/2005.00675/) (2020)
- [Simultaneous paraphrasing and translation by fine-tuning Transformer models](https://mlsanity.com/paper/2005.05570/) (2020)
- [Faster Re-translation Using Non-Autoregressive Model For Simultaneous Neural Machine Translation](https://mlsanity.com/paper/2012.14681/) (2020)

## Built on by (most cited first, 4 of 4)

- [Improving Simultaneous Machine Translation with Monolingual Data](https://mlsanity.com/paper/2212.01188/) (2022)
- [Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair](https://mlsanity.com/paper/2404.12299/) (2024)
- [Word Order in English-Japanese Simultaneous Interpretation: Analyses and Evaluation using Chunk-wise Monotonic Translation](https://mlsanity.com/paper/2406.08940/) (2024)
- [Dual-Stream Simultaneous Translation via 2D Grid Attention](https://mlsanity.com/paper/2609.34902/) (2026)

## Abstract (arXiv)

Recent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing with a language pair whose word orders significantly differ. This is unlike human simultaneous interpreters who produce largely monotonic translations at the expense of the grammaticality of a sentence being translated. In this paper, we thus propose an algorithm to reorder and refine the target side of a full sentence translation corpus, so that the words/phrases between the source and target sentences are aligned largely monotonically, using word alignment and non-autoregressive neural machine translation. We then train a widely used wait-k simultaneous translation model on this reordered-and-refined corpus. The proposed approach improves BLEU scores and resulting translations exhibit enhanced monotonicity with source sentences.