Title:	CULTURAL MACHINE TRANSLATION USING MRF GIBBS MODEL & BAYESIAN LEARNING
Author(s):	Fernand Cohen and Zheng Zhong
ISBN:	978-989-8533-66-1
Editors:	Yingcai Xiao and Ajith P. Abraham
Year:	2017
Edition:	Single
Keywords:	Cultural translation; MRF; Gibbs model; MLE; MAP; Iterative relaxation
Type:	Full Paper
First Page:	223
Last Page:	230
Language:	English
Cover:
Full Contents:	click to dowload
Paper Abstract:	In this paper, we introduce a novel Gibbs language model constructed on a multi-layered dependency semantic graph to lexically disambiguate words, phrases, and sentences that lend themselves to different possible meaning and interpretations for use in machine translation (MT). The model looks at semantic cliques of words (key words) and assigns Gibbs potentials and conditional probabilities in proportion to the importance and degree of interactions between a given word and its neighbors within the semantic clique. Efficient estimates (maximum likelihood estimators (MLE)) for the Gibbs clique parameters are obtained using bilingual parallel corpora. Our method also naturally factors in the beliefs of expert translators, maps them into expert Gibbs parameters, and updates the MLE to maximum a posterior probability (MAP) estimates. Experimental results using our model and method are reported on testbeds in the medical and literary fiction domains and our results fare more than favorably when compared to the state-of-the-art long short-term memory (LSTM) Neural Network (NN) approach.
	Go Back