# Multi-Source Neural Translation

arXiv: https://arxiv.org/abs/1601.00710 · HTML: https://mlsanity.com/paper/1601.00710/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2016.

## Built on by (most cited first, 30 of 37)

- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [Multilingual training for Software Engineering](https://mlsanity.com/paper/2112.02043/) (2021)
- [Language Tags Matter for Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/2106.07930/) (2021)
- [Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation](https://mlsanity.com/paper/2212.10551/) (2022)
- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [Alternative Input Signals Ease Transfer in Multilingual Machine Translation](https://mlsanity.com/paper/2110.07804/) (2021)
- [An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generation](https://mlsanity.com/paper/2212.09387/) (2022)
- [On Measuring Context Utilization in Document-Level MT Systems](https://mlsanity.com/paper/2402.01404/) (2024)
- [Adaptive Sparse Transformer for Multilingual Translation](https://mlsanity.com/paper/2104.07358/) (2021)
- [Revealing the Parallel Multilingual Learning within Large Language Models](https://mlsanity.com/paper/2403.09073/) (2024)
- [YANMTT: Yet Another Neural Machine Translation Toolkit](https://mlsanity.com/paper/2108.11126/) (2021)
- [Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation](https://mlsanity.com/paper/2203.07627/) (2022)
- [Simultaneous Multi-Pivot Neural Machine Translation](https://mlsanity.com/paper/2104.07410/) (2021)
- [Parameter Differentiation based Multilingual Neural Machine Translation](https://mlsanity.com/paper/2112.13619/) (2021)
- [Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Models](https://mlsanity.com/paper/2311.07439/) (2023)
- [Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing](https://mlsanity.com/paper/2406.11085/) (2024)
- [Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages](https://mlsanity.com/paper/2108.07127/) (2021)
- [Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021](https://mlsanity.com/paper/2108.08556/) (2021)
- [Building Multilingual Machine Translation Systems That Serve Arbitrary X-Y Translations](https://mlsanity.com/paper/2206.14982/) (2022)
- [BERTGEN: Multi-task Generation through BERT](https://mlsanity.com/paper/2106.03484/) (2021)
- [ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus](https://mlsanity.com/paper/2107.06632/) (2021)
- [Revisiting Language Encoding in Learning Multilingual Representations](https://mlsanity.com/paper/2102.08357/) (2021)
- [Transfer Learning for Sequence Generation: from Single-source to Multi-source](https://mlsanity.com/paper/2105.14809/) (2021)
- [Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units](https://mlsanity.com/paper/2106.04935/) (2021)
- [One Source, Two Targets: Challenges and Rewards of Dual Decoding](https://mlsanity.com/paper/2109.10197/) (2021)
- [Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language](https://mlsanity.com/paper/2212.08321/) (2022)
- [Robustness of Multi-Source MT to Transcription Errors](https://mlsanity.com/paper/2305.16894/) (2023)
- [Error-Aware Interactive Semantic Parsing of OpenStreetMap](https://mlsanity.com/paper/2106.11739/) (2021)
- [Revisiting Context Choices for Context-aware Machine Translation](https://mlsanity.com/paper/2109.02995/) (2021)

## Abstract (arXiv)

We build a multi-source machine translation model and train it to maximize the probability of a target English string given French and German sources. Using the neural encoder-decoder framework, we explore several combination methods and report up to +4.8 Bleu increases on top of a very strong attention-based neural translation model.