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Statistical Machine Translation for Indic Languages | | Sudhansu Bala Das
; Divyajoti Panda
; Tapas Kumar Mishra
; Bidyut Kr. Patra
; | | Date: |
2 Jan 2023 | | Abstract: | Machine Translation (MT) system generally aims at automatic representation of
source language into target language retaining the originality of context using
various Natural Language Processing (NLP) techniques. Among various NLP
methods, Statistical Machine Translation(SMT). SMT uses probabilistic and
statistical techniques to analyze information and conversion. This paper
canvasses about the development of bilingual SMT models for translating English
to fifteen low-resource Indian Languages (ILs) and vice versa. At the outset,
all 15 languages are briefed with a short description related to our
experimental need. Further, a detailed analysis of Samanantar and OPUS dataset
for model building, along with standard benchmark dataset (Flores-200) for
fine-tuning and testing, is done as a part of our experiment. Different
preprocessing approaches are proposed in this paper to handle the noise of the
dataset. To create the system, MOSES open-source SMT toolkit is explored.
Distance reordering is utilized with the aim to understand the rules of grammar
and context-dependent adjustments through a phrase reordering categorization
framework. In our experiment, the quality of the translation is evaluated using
standard metrics such as BLEU, METEOR, and RIBES | | Source: | arXiv, 2301.00539 | | Services: | Forum | Review | PDF | Favorites | |
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