Abstract:
The process of converting different documents from one natural language to another natural
language by using the language and capability of a computer is a promising point of MT. MT
is one of the basic tasks under computational linguistics that helps for automatic translation
of resources by using different approaches to translation. Those approaches may include
rule-based, example-based, statistical, neural, and hybrid. The main concern with the concept
of hybridization in MT is the integration of two or more MT approaches to implement the
translation process. In this study, we used the hybrid (statistical and rule-based) approach for
the translation of English to the Wolaytta language. The statistical approach works based on
the probability of words in the given sentences, and the rule-based approach works with
different linguistic and translation rules to produce fluent output. Quality of translation is one
of the big issues in MT. Rearrangement of words in SL sentences, to make them in the form
of the TL, is used as a reordering rule for the rule part of our study.
The design part of the study begins with the architecture of the English to Wolaytta HMT
approach. We divided the collected parallel corpora for training and testing purposes. After
pre-processing of the corpus, POS tagging is applied to the collected corpus to identify word
classes for the reordering of words for hybridization. We performed the implementation of
local reordering for source language sentences after POS tagging. We built the translation
model for the SL and the language model for the TL by using the IRSTLM language
modeling tool. Researchers used Moses decoder for the decoding process to see the
translation output. Finally, BLEU score is used for testing. We collected the parallel corpus
from three different sources. We implemented two basic experiments to determine the final
result of the study. The first experiment is to determine the quality of baseline translation for
the SMT approach and recorded 2.37 of BLEU score. Whereas, the second experiment used
locally reordered sentences for the part of a hybrid approach and BLEU score is 3.93. Based
on the experimental result of BLEU, a translation with reordering of words as a reordering
rule has better translation than the statistical approach with +1.56 of BLEU score