# Asynchronous and Segmented Bidirectional Encoding for NMT

arXiv: https://arxiv.org/abs/2402.14849 · HTML: https://mlsanity.com/paper/2402.14849/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [What Makes Convolutional Models Great on Long Sequence Modeling?](https://mlsanity.com/paper/2210.09298/) (2022)
- [Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation](https://mlsanity.com/paper/2212.10551/) (2022)
- [Meta Back-translation](https://mlsanity.com/paper/2102.07847/) (2021)
- [LiteLSTM Architecture for Deep Recurrent Neural Networks](https://mlsanity.com/paper/2201.11624/) (2022)
- [An Overview on Machine Translation Evaluation](https://mlsanity.com/paper/2202.11027/) (2022)
- [FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation](https://mlsanity.com/paper/2012.15717/) (2020)

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

- [TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards](https://mlsanity.com/paper/2605.19320/) (2026)
- [LabGuard: Grounding Natural-Language Laboratory Rules into Runtime Guards for Embodied Laboratory Agents](https://mlsanity.com/paper/2606.31045/) (2026)

## Abstract (arXiv)

With the rapid advancement of Neural Machine Translation (NMT), enhancing translation efficiency and quality has become a focal point of research. Despite the commendable performance of general models such as the Transformer in various aspects, they still fall short in processing long sentences and fully leveraging bidirectional contextual information. This paper introduces an improved model based on the Transformer, implementing an asynchronous and segmented bidirectional decoding strategy aimed at elevating translation efficiency and accuracy. Compared to traditional unidirectional translations from left-to-right or right-to-left, our method demonstrates heightened efficiency and improved translation quality, particularly in handling long sentences. Experimental results on the IWSLT2017 dataset confirm the effectiveness of our approach in accelerating translation and increasing accuracy, especially surpassing traditional unidirectional strategies in long sentence translation. Furthermore, this study analyzes the impact of sentence length on decoding outcomes and explores the model's performance in various scenarios. The findings of this research not only provide an effective encoding strategy for the NMT field but also pave new avenues and directions for future studies.