Learning better name translation for cross-lingual wikification

Abstract

A notable challenge in cross-lingual wikification is the problem of retrieving English Wikipedia title candidates given a non-English mention, a step that requires translating names 1 written in a foreign language into English. Creating training data for name translation requires significant amount of human efforts. In order to cover as many languages as possible, we propose a probabilistic model that leverages indirect supervision signals in a knowledge base. More specifically, the model learns name translation from title pairs obtained from the inter-language links in Wikipedia. The model jointly considers word alignment and word transliteration. Comparing to 6 other approaches on 9 languages, we show that the proposed model outperforms others not only on the transliteration metric, but also on the ability to generate target English titles for a cross-lingual wikifier. Consequently, as we show, it improves the end-to-end performance of a cross-lingual wikifier on the TAC 2016 EDL dataset.