Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and…

Comparison of SMT and RBMT; The Requirement of Hybridization for Marathi-Hindi MT

We present in this paper our work on comparison between Statistical Machine Translation (SMT) and Rule-based machine translation for translation from Marathi to Hindi. Rule Based systems although robust take lots of time…

Retrosynthetic reaction prediction using neural sequence-to-sequence models

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder arch…

Urdu To Punjabi Machine Translation System

Machine Translation is a popular area of NLP research field. There are various approaches to develop a machine translation system like Rule-Based, Statistical, Neural and Hybrid. A rule-Based system is based on grammatic…

A machine translation system combining rule-based machine translation and statistical post-editing