# Neural Machine Translation: Challenges, Progress and Future

arXiv: https://arxiv.org/abs/2004.05809 · HTML: https://mlsanity.com/paper/2004.05809/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

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

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning](https://mlsanity.com/paper/2401.12863/) (2024)
- [Stay Focused: Problem Drift in Multi-Agent Debate](https://mlsanity.com/paper/2502.19559/) (2025)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis](https://mlsanity.com/paper/2303.07201/) (2023)
- [Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?](https://mlsanity.com/paper/2410.17145/) (2024)
- [SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection](https://mlsanity.com/paper/2310.08040/) (2023)
- [Language verY Rare for All](https://mlsanity.com/paper/2412.13924/) (2024)
- [Bridging Language Gaps with Adaptive RAG: Improving Indonesian Language Question Answering](https://mlsanity.com/paper/2510.21068/) (2025)
- [NSL-MT: Linguistically Informed Negative Samples for Efficient Machine Translation in Low-Resource Languages](https://mlsanity.com/paper/2511.09537/) (2025)

## Abstract (arXiv)

Machine translation (MT) is a technique that leverages computers to translate human languages automatically. Nowadays, neural machine translation (NMT) which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of MT. This article makes a review of NMT framework, discusses the challenges in NMT, introduces some exciting recent progresses and finally looks forward to some potential future research trends. In addition, we maintain the state-of-the-art methods for various NMT tasks at the website https://github.com/ZNLP/SOTA-MT.