Machine Translation bridges communication barriers and eases interaction among people having different linguistic backgrounds. Machine Translation mechanisms exploit a range of techniques and linguistic resources for translation prediction. Neural machine translation (NMT), in particular, seeks optimality in translation through training of neural network, using a parallel corpus having a considerable number of instances in the form of a parallel running source and target sentences. Easy availability of parallel corpora for major Indian language forms and the ability of NMT systems to better analyze context and produce fluent translation make NMT a prominent choice for the translation of Indian languages. We have trained, tested, and analyzed NMT systems for English to Tamil, English to Hindi, and English to Punjabi translations. Predicted translations have been evaluated using Bilingual Evaluation Understudy and by human evaluators to assess the quality of translation in terms of its adequacy, fluency, and correspondence with human-predicted translation.

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Effective retrieval of mathematical contents from vast corpus of scientific documents demands enhancement in the conventional indexing and searching mechanisms. Indexing mechanism and the choice of semantic similarity measures guide the results of Math Information Retrieval system (MathIRs) to perfection. Tokenization and formula unification are among the distinguishing features of indexing mechanism, used in MathIRs, which facilitate sub-formula and similarity search. Besides, the scientific documents and the user queries in MathIRs will contain math as well as text contents and to match these contents we require three important modules: Text-Text Similarity (TS), Math-Math Similarity (MS) and Text-Math Similarity (TMS). In this paper we have proposed MathIRs comprising these important modules and a substitution tree based mechanism for indexing mathematical expressions. We have also presented experimental results for similarity search and argued that proposal of MathIRs will ease the task of scientific document retrieval.