Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages
This work addresses the challenge of multilingual knowledge acquisition for low-resource languages by making transliteration generation more accessible, though it is incremental as it builds on existing transliteration discovery methods.
The paper tackles the problem of generating English transliterations for names in low-resource languages, which typically requires large training datasets, by introducing a bootstrapping algorithm that reduces the needed examples to as few as 500, enabling faster annotation and application to languages with limited data.
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.