# Local Translation Services for Neglected Languages

arXiv: https://arxiv.org/abs/2101.01628 · HTML: https://mlsanity.com/paper/2101.01628/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages](https://mlsanity.com/paper/2012.05628/) (2020)
- [A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation](https://mlsanity.com/paper/1711.01921/) (2017)
- [A Comprehensive Survey of Multilingual Neural Machine Translation](https://mlsanity.com/paper/2001.01115/) (2020)
- [Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation](https://mlsanity.com/paper/1602.04874/) (2016)
- [Where to put the Image in an Image Caption Generator](https://mlsanity.com/paper/1703.09137/) (2017)
- [Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models](https://mlsanity.com/paper/2006.11130/) (2020)

## Built on by (most cited first, 2 of 2)

- [Professional Certification Benchmark Dataset: The First 500 Jobs For Large Language Models](https://mlsanity.com/paper/2305.05377/) (2023)
- [Evaluating AI Vocational Skills Through Professional Testing](https://mlsanity.com/paper/2312.10603/) (2023)

## Abstract (arXiv)

Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages. Locally run translators for less popular languages may assist data projects with protected or personal data that may require specific compliance checks before posting to a public translation API, but which could render reasonable, cost-effective solutions if done with an army of local, small-scale pair translators. Like handling a specialist's dialect, this research illustrates translating two historically interesting, but obfuscated languages: 1) hacker-speak ("l33t") and 2) reverse (or "mirror") writing as practiced by Leonardo da Vinci. The work generalizes a deep learning architecture to translatable variants of hacker-speak with lite, medium, and hard vocabularies. The original contribution highlights a fluent translator of hacker-speak in under 50 megabytes and demonstrates a generator for augmenting future datasets with greater than a million bilingual sentence pairs. The long short-term memory, recurrent neural network (LSTM-RNN) extends previous work demonstrating an English-to-foreign translation service built from as little as 10,000 bilingual sentence pairs. This work further solves the equivalent translation problem in twenty-six additional (non-obfuscated) languages and rank orders those models and their proficiency quantitatively with Italian as the most successful and Mandarin Chinese as the most challenging. For neglected languages, the method prototypes novel services for smaller niche translations such as Kabyle (Algerian dialect) which covers between 5-7 million speakers but one which for most enterprise translators, has not yet reached development. One anticipates the extension of this approach to other important dialects, such as translating technical (medical or legal) jargon and processing health records.