Abstract: DeConverter is core software in a Universal Networking Language (UNL) system. A UNL system has EnConverter and DeConverter as its two major components. EnConverter is used to convert a natural language sentence into an equivalent UNL expression, and DeConverter is used to generate a natural language sentence from an input UNL expression. This paper presents design and development of a Punjabi DeConverter. It describes five phases of the proposed Punjabi DeConverter, i.e., UNL parser, lexeme selection, morphology generation, function word insertion, and syntactic linearization. This paper also illustrates all these phases of the Punjabi DeConverter with a special focus on syntactic linearization issues of the Punjabi DeConverter. Syntactic linearization is the process of defining arrangements of words in generated output. The algorithms and pseudocodes for implementation of syntactic linearization of a simple UNL graph, a UNL graph with scope nodes and a node having un-traversed parents or multiple parents in a UNL graph have been discussed in this paper. Special cases of syntactic linearization with respect to Punjabi language for UNL relations like ‘and’, ‘or’, ‘fmt’, ‘cnt’, and ‘seq’ have also been presented in this paper. This paper also provides implementation results of the proposed Punjabi DeConverter. The DeConverter has been tested on 1000 UNL expressions by considering a Spanish UNL language server and agricultural domain threads developed by Indian Institute of Technology (IIT), Bombay, India, as gold-standards. The proposed system generates 89.0% grammatically correct sentences, 92.0% faithful sentences to the original sentences, and has a fluency score of 3.61 and an adequacy score of 3.70 on a 4-point scale. The system is also able to achieve a bilingual evaluation understudy (BLEU) score of 0.72.

Key words: DeConverter, EnConverter, Machine translation, Universal Networking Language (UNL), Syntactic linearization

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