# Modeling Dual Read/Write Paths for Simultaneous Machine Translation

arXiv: https://arxiv.org/abs/2203.09163 · HTML: https://mlsanity.com/paper/2203.09163/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Dual Learning for Machine Translation](https://mlsanity.com/paper/1611.00179/) (2016)
- [Monotonic Infinite Lookback Attention for Simultaneous Machine Translation](https://mlsanity.com/paper/1906.05218/) (2019)
- [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)
- [Online and Linear-Time Attention by Enforcing Monotonic Alignments](https://mlsanity.com/paper/1704.00784/) (2017)
- [Efficient Wait-k Models for Simultaneous Machine Translation](https://mlsanity.com/paper/2005.08595/) (2020)
- [Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation](https://mlsanity.com/paper/1806.03661/) (2018)
- [An Effective Approach to Unsupervised Machine Translation](https://mlsanity.com/paper/1902.01313/) (2019)
- [Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation](https://mlsanity.com/paper/1909.01559/) (2019)
- [Dual Supervised Learning](https://mlsanity.com/paper/1707.00415/) (2017)
- [Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy](https://mlsanity.com/paper/2109.05238/) (2021)
- [Neural Machine Translation with Supervised Attention](https://mlsanity.com/paper/1609.04186/) (2016)
- [What does Attention in Neural Machine Translation Pay Attention to?](https://mlsanity.com/paper/1710.03348/) (2017)
- [Future-Guided Incremental Transformer for Simultaneous Translation](https://mlsanity.com/paper/2012.12465/) (2020)
- [Simultaneous Translation with Flexible Policy via Restricted Imitation Learning](https://mlsanity.com/paper/1906.01135/) (2019)
- [Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning](https://mlsanity.com/paper/2002.04306/) (2020)
- [Neural Simultaneous Speech Translation Using Alignment-Based Chunking](https://mlsanity.com/paper/2005.14489/) (2020)
- [Zero-Shot Dual Machine Translation](https://mlsanity.com/paper/1805.10338/) (2018)
- [Infusing Future Information into Monotonic Attention Through Language Models](https://mlsanity.com/paper/2109.03121/) (2021)

## Built on by (most cited first, 21 of 21)

- [Information-Transport-based Policy for Simultaneous Translation](https://mlsanity.com/paper/2210.12357/) (2022)
- [Hidden Markov Transformer for Simultaneous Machine Translation](https://mlsanity.com/paper/2303.00257/) (2023)
- [End-to-End Simultaneous Speech Translation with Differentiable Segmentation](https://mlsanity.com/paper/2305.16093/) (2023)
- [StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning](https://mlsanity.com/paper/2406.03049/) (2024)
- [Wait-info Policy: Balancing Source and Target at Information Level for Simultaneous Machine Translation](https://mlsanity.com/paper/2210.11220/) (2022)
- [Learning Optimal Policy for Simultaneous Machine Translation via Binary Search](https://mlsanity.com/paper/2305.12774/) (2023)
- [Turning Fixed to Adaptive: Integrating Post-Evaluation into Simultaneous Machine Translation](https://mlsanity.com/paper/2210.11900/) (2022)
- [Decoder-only Streaming Transformer for Simultaneous Translation](https://mlsanity.com/paper/2406.03878/) (2024)
- [Glancing Future for Simultaneous Machine Translation](https://mlsanity.com/paper/2309.06179/) (2023)
- [Simultaneous Machine Translation with Tailored Reference](https://mlsanity.com/paper/2310.13588/) (2023)
- [Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference](https://mlsanity.com/paper/2303.07914/) (2023)
- [Unified Segment-to-Segment Framework for Simultaneous Sequence Generation](https://mlsanity.com/paper/2310.17940/) (2023)
- [Self-Modifying State Modeling for Simultaneous Machine Translation](https://mlsanity.com/paper/2406.02237/) (2024)
- [LEAPT: Learning Adaptive Prefix-to-prefix Translation For Simultaneous Machine Translation](https://mlsanity.com/paper/2303.11750/) (2023)
- [Enhanced Simultaneous Machine Translation with Word-level Policies](https://mlsanity.com/paper/2310.16417/) (2023)
- [Language Model is a Branch Predictor for Simultaneous Machine Translation](https://mlsanity.com/paper/2312.14488/) (2023)
- [Agent-SiMT: Agent-assisted Simultaneous Machine Translation with Large Language Models](https://mlsanity.com/paper/2406.06910/) (2024)
- [CBSiMT: Mitigating Hallucination in Simultaneous Machine Translation with Weighted Prefix-to-Prefix Training](https://mlsanity.com/paper/2311.03672/) (2023)
- [R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation](https://mlsanity.com/paper/2401.05700/) (2024)
- [On the Hallucination in Simultaneous Machine Translation](https://mlsanity.com/paper/2406.07239/) (2024)
- [Context Consistency between Training and Testing in Simultaneous Machine Translation](https://mlsanity.com/paper/2311.07066/) (2023)

## Abstract (arXiv)

Simultaneous machine translation (SiMT) outputs translation while reading source sentence and hence requires a policy to decide whether to wait for the next source word (READ) or generate a target word (WRITE), the actions of which form a read/write path. Although the read/write path is essential to SiMT performance, no direct supervision is given to the path in the existing methods. In this paper, we propose a method of dual-path SiMT which introduces duality constraints to direct the read/write path. According to duality constraints, the read/write path in source-to-target and target-to-source SiMT models can be mapped to each other. As a result, the two SiMT models can be optimized jointly by forcing their read/write paths to satisfy the mapping. Experiments on En-Vi and De-En tasks show that our method can outperform strong baselines under all latency.