Learning Better Name Translation for Cross-Lingual Wikification

Authors

  • Chen-Tse Tsai Bloomberg LP
  • Dan Roth University of Pennsylvania

Keywords:

Information Extraction, Name Translation, Cross-Lingual, Entity Llinking, 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 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.

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Published

2018-04-27

How to Cite

Tsai, C.-T., & Roth, D. (2018). Learning Better Name Translation for Cross-Lingual Wikification. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12018