# Machine Translation to Control Formality Features in the Target Language

arXiv: https://arxiv.org/abs/2311.13475 · HTML: https://mlsanity.com/paper/2311.13475/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages](https://mlsanity.com/paper/2212.05409/) (2022)
- [Getting Gender Right in Neural Machine Translation](https://mlsanity.com/paper/1909.05088/) (2019)
- [Controlling the Output Length of Neural Machine Translation](https://mlsanity.com/paper/1910.10408/) (2019)
- [Multi-Task Neural Models for Translating Between Styles Within and Across Languages](https://mlsanity.com/paper/1806.04357/) (2018)
- [Controlling Neural Machine Translation Formality with Synthetic Supervision](https://mlsanity.com/paper/1911.08706/) (2019)
- [Controlling Translation Formality Using Pre-trained Multilingual Language Models](https://mlsanity.com/paper/2205.06644/) (2022)
- [Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022](https://mlsanity.com/paper/2205.05990/) (2022)

## Abstract (arXiv)

Formality plays a significant role in language communication, especially in low-resource languages such as Hindi, Japanese and Korean. These languages utilise formal and informal expressions to convey messages based on social contexts and relationships. When a language translation technique is used to translate from a source language that does not pertain the formality (e.g. English) to a target language that does, there is a missing information on formality that could be a challenge in producing an accurate outcome. This research explores how this issue should be resolved when machine learning methods are used to translate from English to languages with formality, using Hindi as the example data. This was done by training a bilingual model in a formality-controlled setting and comparing its performance with a pre-trained multilingual model in a similar setting. Since there are not a lot of training data with ground truth, automated annotation techniques were employed to increase the data size. The primary modeling approach involved leveraging transformer models, which have demonstrated effectiveness in various natural language processing tasks. We evaluate the official formality accuracy(ACC) by comparing the predicted masked tokens with the ground truth. This metric provides a quantitative measure of how well the translations align with the desired outputs. Our study showcases a versatile translation strategy that considers the nuances of formality in the target language, catering to diverse language communication needs and scenarios.