1 Introduction
Table 11: Performance on NEWS 2010 En-Hi Transliteration Mining Dataset
['Model', 'F1-measure (%)']
For testing we consider the standard NEWS 2010 transliteration mining test set ( This test set contains approximately 1000 Wikipedia English Hindi title pairs. The original task definition is as follows. For a given English title containing T1 words and the corresponding Hindi title containing T2 words identify all pairs which form a transliteration pair. Specifically, for each title pair, consider all T1×T2 word pairs and identify the correct transliteration pairs. In all, the test set contains 5468 word pairs out of which 982 are transliteration pairs. For every word pair (xi,yi) we obtain a 50 dimensional common representation for xi and yi using the trained CorrNet. We then calculate the correlation between the representations of xi and yi. If the correlation is above a threshold we mark the word pair as equivalent. This threshold is tuned using an additional 1000 pairs which were provided as training data for the NEWS 2010 transliteration mining task. Note that our aim is not to achieve state of the art performance on this task but to compare the quality of the shared representations learned using different CRL methods considered in this paper.