# A Survey of Orthographic Information in Machine Translation

arXiv: https://arxiv.org/abs/2008.01391 · HTML: https://mlsanity.com/paper/2008.01391/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin](https://mlsanity.com/paper/2307.00382/) (2023)
- [From Plenipotentiary to Puddingless: Users and Uses of New Words in Early English Letters](https://mlsanity.com/paper/2103.09926/) (2021)
- [Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021](https://mlsanity.com/paper/2108.08556/) (2021)
- [PICT@DravidianLangTech-ACL2022: Neural Machine Translation On Dravidian Languages](https://mlsanity.com/paper/2204.09098/) (2022)
- [Machine Translation by Projecting Text into the Same Phonetic-Orthographic Space Using a Common Encoding](https://mlsanity.com/paper/2305.12371/) (2023)

## Abstract (arXiv)

Machine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable, but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography's influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts of cognates information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction.