Developing a Chunk-based Grammar Checker for Translated English Sentences

Developing a Chunk-based Grammar Checker for Translated English Sentences Nay Yee Lin a, Khin Mar Soe b, and Ni Lar Thein c a,cUniversity of Computer Studies,Yangon, Myanmar {nayyeelynn, nilarthein}@gmail.com bNatural Language Processing Laboratory, University of Computer Studies,Yangon, Myanmar kmsucsy@gmail.com Abstract. Machine Translation systems expect target language output to be grammatically correct. In Myanmar-English statistical machine translation system, target language output (English) can often be ungrammatical. To address this issue, we propose an ongoing chunk-based grammar checker by using trigram language model and rule based model. It is able to solve distortion, deficiency and make smooth the translated English sentences. We identify the sentences with chunk levels and generate context free grammar (CFG) rules for recognizing grammatical relations of chunks. There are three main processes to build a grammar checker: checking the sentence patterns in chunk level, analyzing the chunk errors and correcting the errors. According to experimental results, this checker can detect simple, compound and complex sentence types for declarative and interrogative sentences. This system is useful for reducing grammar errors of target language in Myanmar-English machine translation system. Keywords: Statistical Machine Translation System, Trigram Language Model, Rule based Model. 1 Introduction A language checker, typically, has two basic components, a spell-checker and a grammar checker. Whereas the spell-checker, usually, limits its operation to the inspection and correction of individual text words, the grammar checker has to cope with errors that can only be detected in contexts that are larger than the word (Anna). Grammar is the set of structural rules that govern the composition of clauses, phrases, chunks and words in any given natural language. Grammar checking is one of the most widely used tools within natural language processing (NLP) applications. Grammar checkers check the grammatical structure of sentences based on morphological processing and syntactic processing. These two steps are part of natural language processing to understand the natural languages. Morphological processing is the step where individual words are analyzed into their components and non-word tokens such as punctuation. Syntactic processing is the analysis where linear sequences of words are transformed into structures that show grammatical relationships between the words in the sentence (Rich and Knight 1991). Three main approaches are widely used for grammar checking in a language; syntax-based checking, statistics-based checking and rule-based checking. In syntax based grammar checking, each sentence is completely parsed to check the grammatical correctness of it. The text is considered incorrect if the syntactic parsing fails. In statistics-based approach, POS tag sequences are built from an annotated corpus, and the frequency, and thus the probability, of these sequences are noted. The text is considered incorrect if the POS-tagged text contains POS 25th Pacific Asia Conference on Language, Information and Computation, pages 245–254 245 sequences with frequencies lower than some threshold. The statistics based approach essentially learns the rules from the tagged training corpus. In rule-based approach, the approach is very similar to the statistics based one, except that the rules must be handcrafted (Naber, 2003). Grammar checkers are most often implemented as …

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