# Prediction of Translation Techniques for the Translation Process

arXiv: https://arxiv.org/abs/2403.14454 · HTML: https://mlsanity.com/paper/2403.14454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 21 Mar 2024.

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

- [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)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [Achieving Human Parity on Automatic Chinese to English News Translation](https://mlsanity.com/paper/1803.05567/) (2018)
- [Scaling Laws for Neural Machine Translation](https://mlsanity.com/paper/2109.07740/) (2021)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Neural Machine Translation: A Review and Survey](https://mlsanity.com/paper/1912.02047/) (2019)
- [Examining the Tip of the Iceberg: A Data Set for Idiom Translation](https://mlsanity.com/paper/1802.04681/) (2018)
- [Do GPTs Produce Less Literal Translations?](https://mlsanity.com/paper/2305.16806/) (2023)
- [Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder](https://mlsanity.com/paper/1707.05436/) (2017)
- [Improving Machine Translation with Phrase Pair Injection and Corpus Filtering](https://mlsanity.com/paper/2301.08008/) (2023)
- [Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors](https://mlsanity.com/paper/2302.08975/) (2023)
- [Towards Recognizing Phrase Translation Processes: Experiments on English-French](https://mlsanity.com/paper/1904.12213/) (2019)

## Abstract (arXiv)

Machine translation (MT) encompasses a variety of methodologies aimed at enhancing the accuracy of translations. In contrast, the process of human-generated translation relies on a wide range of translation techniques, which are crucial for ensuring linguistic adequacy and fluency. This study suggests that these translation techniques could further optimize machine translation if they are automatically identified before being applied to guide the translation process effectively. The study differentiates between two scenarios of the translation process: from-scratch translation and post-editing. For each scenario, a specific set of experiments has been designed to forecast the most appropriate translation techniques. The findings indicate that the predictive accuracy for from-scratch translation reaches 82%, while the post-editing process exhibits even greater potential, achieving an accuracy rate of 93%.