# Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages

arXiv: https://arxiv.org/abs/1809.07807 · HTML: https://mlsanity.com/paper/1809.07807/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Exploiting All Samples in Low-Resource Sentence Classification: Early Stopping and Initialization Parameters](https://mlsanity.com/paper/2111.06971/) (2021)
- [Efficient Entity Candidate Generation for Low-Resource Languages](https://mlsanity.com/paper/2206.15163/) (2022)
- [When Names Cross Scripts: A Source-Grounded Benchmark for Historical Entity Reconciliation in the Mongol World](https://mlsanity.com/paper/2608.23507/) (2026)

## Abstract (arXiv)

Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction. Existing approaches to transliteration generation require a large (>5000) number of training examples. This difficulty contrasts with transliteration discovery, a somewhat easier task that involves picking a plausible transliteration from a given list. In this work, we present a bootstrapping algorithm that uses constrained discovery to improve generation, and can be used with as few as 500 training examples, which we show can be sourced from annotators in a matter of hours. This opens the task to languages for which large number of training examples are unavailable. We evaluate transliteration generation performance itself, as well the improvement it brings to cross-lingual candidate generation for entity linking, a typical downstream task. We present a comprehensive evaluation of our approach on nine languages, each written in a unique script.