TREE ADJOINING GRAMMAR BASED “ LANGUAGE INDEPENDENT GENERATOR ” (2020)

Abstract: This paper proposes language independent natural language generator for Tree Adjoining Grammar (TAG)[8] based Machine Translation System. In this model, the TAG based parsing and generation approach considered for the syntactic and semantic analysis of a source language. This model provides an efficient and a systematic way of encapsulating language resources with engineering solution to develop the machine translation System. A TAG based Generator is developed with existing resources using TAG formalism to generate the target language from TAG based parser derivation. The process allows syntactic feature-marking, the Subject-Predicate Agreement marking and multiple synthesized generated outputs in complex and morphological rich language. The challenge in applying such approach is to handle the linguistically diversified features. It is achieved using rule-based translation grammar model to align the source language to corresponding target languages. The computational experiments demonstrate that substantial performance in terms of time and memory could also be obtained by using this approach. Nevertheless, this paper also describes the process of lexicalization and explain the state charts, TAG based adjunction and substitution function and the complexity and challenges beneath parsing-generation process.

## introduction machine translation is a sub-field of (computational linguistics) under natural language processing (nlp) where computer is act as a human translator. it processes natural language constructs to automate the process of language translation. every machine translation system requires programs for translation and automated dictionaries and grammars to support translation. among the various statistical and rule based methodologies, we have researched on grammar based model for english to indian language translation. lexicalized tree adjoining grammar (ltag) is a non chomsky formalism initially proposed in @xcite considered to be mildly context grammar that is ideal for any natural language. the proposed translation scheme is based on compatible tags of the source and target languages. a tag parser is act as compliers used to analyze the source sentence based on the tree adjoining grammar and construct the source derivation which is the summarization of the state chart processing. tag generator is like an interpreter that interprets the source derivation to a target derivation and lexicalizes the target derivation into derived tree generator which gives us the target sentence accordingly. the basic motivation to use this tag grammar model is that it is a rule based language independent feature oriented approach. any complex agglutinative language can be represented using tags and their unification features. the translation accuracy of this approach depends on the tree grammar rather than "bag of corpus". while any statistical approach translation accuracy depends on the corpus, more the corpus size better will be the output. we can still build a robust model for indian languages with complex verb and noun morphology. generation of the tree grammar for the source and target language requires good linguistic knowledge and expertise for providing feature with the grammar. related research work of tag generator in nlp is presented in section-2 as literature survey. section-3 elaborates more about basic flow of tag generator considering the tag based mts. detailed architecture along with internal modules description have also been explained in this section. while section-4 talks about results and analysis. conclusion of the paper is done in section-5. ## literature survey there has been a fair amount of research in the field of tree adjoining grammar based generation of machine translation (mt). some closely related research work @xcite is reported to address the similar work. @xcite discussed that tag may be an appropriate formalism for generation because of their syntactic ## workflow of tag generator as earlier said, tag generation act like interpreters, just like java virtual machine (jvm) in the java programming language which interpret the byte code to machine code, like wise tag generator interpret the source derivation to target language. tag generation compresses into three parts: transfer model, derivation generation, derived tree generator. as shown in figure 1 , transfer model is a linguistic based model in which source tree is mapped with the target tree i.e. every node of the source tree is mapped with the node of target tree which is called as link information. link information is an agreement between a source tree and the target tree, illustrate the node mapping between the language pair. derivation generation process the derivation parser (byte code in java) convert it into intermediate derivation understand by the generator intermediate process called the derived tree generator. derived tree generator is the lexicalization process of the derivation generator which gives us the target output and language based feature required for synthesis. ## results and experiments sample sentences covering different type of grammatical structures were generated using tag generator in different languages. one of the examples taken from english to hindi translation process, here figure 7 and figure8 depict derivation trees created in source and target language. it indicates syntactic relationship between word and how this information has utilized during translation process. here tree vector (see figure 6 ) is also shown for source sentence. ## conclusion in this paper, we have discussed some of the major tasks involved in the development of tag generator using tag formalism for translation from english to hindi language. all the examples illustrated above are taken from the output generated by the machine translation system developed by us. the effectiveness of this approach is tested by experiments on sample corpus abstract taken from existing resources. we have implemented tag generator as a part of research in machine translation system based on tree adjoining grammar. firstly, we carried out experiments on web application (integrated mt system using tag based parser and generator) running on windows and linux platform to provide a baseline. we have demonstrated that parser and generator are two core components in machine translation system. the system can handle simple and complex sentences with considerably good accuracy rate.