license:openrail region:us
---
license: openrail
---
Our proposed framework can be applied to utilize the beauty of SMT system for building end-to-end translation architecture. The widely used statistical machine translation toolkit
(moses) can also be improved with the proposed framework. Statistical and neural based classifiers are introduced as a bridge between the SMT decoder and random input text. Here,
the required machine translation parameters for decoding are dynamically modified as per requirements for better resource utilization. 


Code: The source code for the proposed work
Dataset and text_data: Prepared datasets

The detailed readme is available inside the respective folders.

# Cite:

The Decoder Framework for Optimized Pruning Strategy in Faster Statistical Machine Translation
Debajyoty Banik