Machine Translation of LATEX Based Mathematical Equations to Spoken Mathematics

Abstract

This paper describes the machine translation of LATEX encoded mathematical equations to spoken mathematical sentences. A LATEX- Spoken math parallel corpus (5,600 sentences) was developed. In this paper, the 10-fold cross-validation experiments were carried out by applying Phrase-based Statistical Machine Translation (PBSMT), Weighted Finite-State Transducers (WFST) and Ripple Down Rules (RDR) based tagging approaches. The BLEU, RIBES, F1 and WER evaluation scoring metrics are used for measuring translation performance. The experimental results show that the PBSMT approach achieved the highest translation performance for LATEX mathematical equations to spoken mathematical sentences translation. Moreover, we found that the translation performance of RDR approach is comparable with PBSMT.