Robust Document Representations for Cross-Lingual Information Retrieval in Low-Resource Settings

The goal of cross-lingual information retrieval (CLIR) is to find relevant documents written in languages different from that of the query. Robustness to translation errors is one of the main challenges for CLIR, especially in low-resource settings where there is limited training data for building machine translation (MT) systems or bilingual dictionaries. If the test collection contains speech documents, additional errors from automatic speech recognition (ASR) makes translation even more difficult. We propose a robust document representation that combines N-best translations and a novel bag-of-phrases output from various ASR/MT systems. We perform a comprehensive empirical analysis on three challenging collections; they consist of Somali, Swahili, and Tagalog speech/text documents to be retrieved by English queries. By comparing various ASR/MT systems with different error profiles, our results demonstrate that a richer document representation can consistently overcome issues in low translation accuracy for CLIR in low-resource settings.