Modelling of terms across scripts through autoencoders

cripts (e.g., Arabic, Greek and Indic languages) one can often find a large amount of user generated transliterated content on the Web in the Roman script. Such content creates a monolingual or cross-lingual space with more than one scripts which is referred as mixed-script space and information retrieval in this space is referred as mixed-script information retrieval (MSIR) [1]. In mixed-script space, the documents and queries may either be in the native script and/or the Roman transliterated script for a language (mono-lingual scenario). There can be further extension of MSIR such as multi-lingual MSIR in which terms can be in multiple scripts in multiple languages. Since there are no standard ways of spelling a word in a non-native script, transliteration content almost always features extensive spelling variations. This phenomenon presents a non-trivial term matching problem for search engines to match the native-script or Roman-transliterated query with the documents in multiple scripts taking into account the spelling variations. This problem, although prevalent inWeb search for users of many languages around the world, has received very little attention till date. Very recently we have formally defined the problem of MSIR and presented the quantitative study on it through Bing query log analysis.