# Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning

arXiv: https://arxiv.org/abs/2002.04306 · HTML: https://mlsanity.com/paper/2002.04306/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Language-conditioned policies and VLAs, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 11 Feb 2020.

## Placed in

- Offline RL and imitation learning from data / [Language-conditioned policies and VLAs](https://mlsanity.com/offlinerl/language-conditioned-policies-and-vlas/)

## Builds on (most cited first, 19 of 19)

- [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 by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [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)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Bridging the Gap between Training and Inference for Neural Machine Translation](https://mlsanity.com/paper/1906.02448/) (2019)
- [Correcting Length Bias in Neural Machine Translation](https://mlsanity.com/paper/1808.10006/) (2018)
- [Direct speech-to-speech translation with a sequence-to-sequence model](https://mlsanity.com/paper/1904.06037/) (2019)
- [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)
- [Recent Advances in Imitation Learning from Observation](https://mlsanity.com/paper/1905.13566/) (2019)
- [Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation](https://mlsanity.com/paper/1806.03661/) (2018)
- [Low-Latency Neural Speech Translation](https://mlsanity.com/paper/1808.00491/) (2018)
- [Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation](https://mlsanity.com/paper/1909.01559/) (2019)
- [Thinking Slow about Latency Evaluation for Simultaneous Machine Translation](https://mlsanity.com/paper/1906.00048/) (2019)
- [Simultaneous Translation with Flexible Policy via Restricted Imitation Learning](https://mlsanity.com/paper/1906.01135/) (2019)
- [Parallel Scheduled Sampling](https://mlsanity.com/paper/1906.04331/) (2019)

## Built on by (most cited first, 10 of 10)

- [Modeling Dual Read/Write Paths for Simultaneous Machine Translation](https://mlsanity.com/paper/2203.09163/) (2022)
- [Gaussian Multi-head Attention for Simultaneous Machine Translation](https://mlsanity.com/paper/2203.09072/) (2022)
- [Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework](https://mlsanity.com/paper/2203.09053/) (2022)
- [Simultaneous Machine Translation with Large Language Models](https://mlsanity.com/paper/2309.06706/) (2023)
- [Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation](https://mlsanity.com/paper/2102.11387/) (2021)
- [It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data](https://mlsanity.com/paper/2110.05213/) (2021)
- [LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline](https://mlsanity.com/paper/2504.09570/) (2025)
- [Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models](https://mlsanity.com/paper/2402.10552/) (2024)
- [Supervised Visual Attention for Simultaneous Multimodal Machine Translation](https://mlsanity.com/paper/2201.09324/) (2022)
- [NAIST-SIC-Aligned: an Aligned English-Japanese Simultaneous Interpretation Corpus](https://mlsanity.com/paper/2304.11766/) (2023)

## Abstract (arXiv)

We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies. First, wepresent an algorithmic oracle to produce oracle READ/WRITE actions for training bilingual sentence-pairs using the notion of word alignments. This oracle actions are designed to capture enough information from the partial input before writing the output. Next, we perform a coupled scheduled sampling to effectively mitigate the exposure bias when learning both policies jointly with imitation learning. Experiments on six language-pairs show our method outperforms strong baselines in terms of translation quality while keeping the translation delay low.