TIAD Shared Task 2019: Orthonormal Explicit Topic Analysis for Translation Inference across Dictionaries

The task of inferring translations can be achieved by the means of comparable corpora and in this paper we apply explicit topic modelling over comparable corpora to the task of inferring translation candidates. In particular, we use the Orthonormal Explicit Topic Analysis (ONETA) model, which has been shown to be the state-of-the-art explicit topic model through its elimination of correlations between topics. The method proves highly effective at selecting translations with high precision.