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A Bicolano-To-Tagalog Transfer-Based Machine Translation System

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A Bicolano-To-Tagalog Transfer-Based Machine Translation System International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-2S8, August 2019 A Bicolano-to-Tagalog Transfer-Based Machine Translation System Ria Ambrocio Sagum Abstract—The Bicolano-Tagalog Transfer-based Machine issue can be attributed to the reason why MT researchers Translation System is a unidirectional machine translator for opted to use statistical based MT systems. languages Bicolano and Tagalog. The transfer-based approach is In the development of MT system for dialects, a researcher divided into three phase: Pre-Processing Analysis, Morphological needs to consider different strategy [6][7]: whether Transfer, and Sentence Generation. The system analyze first the source language (Bicolano) input to create some internal translation from source language to target language takes representation. This includes the tokenizer, stemmer, POS tag and place in a single stage (direct translation), two stages (via an parser. Through transfer rules, it then typically manipulates this ‘interlingua’), or via the ‘transfer’ approach, where the internal representation to transfer parsed source language translation proceeds in the three stages [8][9][10]. syntactic structure into target language syntactic structure. The use of transfer-based approach does not require large Finally, the system generates Tagalog sentence from own amount of word aligned data for translation that is not easily morphological and syntactic information. Each phase will undergo training and evaluation test for the competence of available for Philippine dialects. In transfer-based machine end-results. Overall performance shows a 71.71% accuracy rate. translation, the text input is transformed into an abstract representation, and then its equivalent in the target language Keywords: Machine Translation, Transfer-based, POS tagging, is generated using bilingual dictionaries and grammar rules Morphological Transfer, language model, Language Translation. [11]. Morphology is important area in translation. Different I. INTRODUCTION research projects on morphological analysis and stemming Philippines is considered as one of the countries with rich present in the field are the proofs of its importance [12]. The culture when it comes to language. The reason behind this is most spoken dialect of the Philippines, Tagalog, there are because it is composed of many islands and regions that different studies in analyzing its morphology. A study called allows the people in this country to speak different dialects TAGMA (Tagalog Morphological Analyzer), is a that only locals can communicate with [1]. morphological analyzer, based on the Optimal Theory and To date Philippines have listed 187 languages. Of these, level two morphology that handles Tagalog verbs. [13]. For four are already extinct and 25 are endangered to be extinct. Tagalog dialect a research called TagSa, a Tagalog Stemming The reason behind this can be the non-usage of these Algorithm, was developed for all forms of Tagalog words. It languages by their natives since instead of teaching their can be used specifically for morphological analysis to derive children to speak their native language they teach them a roots [14]. A good accuracy of these tools can lead to a better language that is commonly used by many to easily translation accuracy. communicate with others [2]. There are various means for evaluating the output quality With this, researchers focus on exploring dialects and of machine translation systems. The oldest is the use of device solutions to allow a clear communication amongst human judges to assess a translation's quality [15]. Even Filipinos. The rich language diversity of the Philippines is though human evaluation is time-consuming, it is still the interesting and should be focused on by the researchers in the most reliable method to compare different systems such as field of computing. Using these dialects as the domain of rule-based and statistical systems [16]. Some researchers use Machine Translation (MT) techniques may help us gain more BLEU [17] although some researchers still use the quality insights on language structure, discover language issues that testing thru human evaluation. have been addressed and come up with a solution that may This study evaluated different morphological and transfer contribute to MT in general. approaches to determine its appropriateness to Tagalog and Different issues arise when developing a translator. [3][4] Bicolano translation. In this study translation will focus on One main problem with machine translation (MT) for building morphological tools for analysis of the source and different dialects is the difficulty in acquiring dictionaries translation tools to be used in the translation to the target and grammar rules. So is the determination of file system to language. As a follow up in the previous unpublished paper, be used [5]. Lack of structure rules and the long amount of it will focus on the different tools that will be used starting time it will take to acquire knowledge about a language. This from the lexical analysis, syntax and semantic analysis until Revised Version Manuscript Received on August 19, 2019. RiaAmbrocioSagum, Department of Computer Science,College of Computer and Information Sciences, Polytechnic University of the Philipines, Manila, Philippines. Published By: Retrieval Number: B10620882S819/2019©BEIESP Blue Eyes Intelligence Engineering DOI:10.35940/ijrte.B1062.0882S819 1324 & Sciences Publication A Bicolano-To-Tagalog Transfer-Based Machine Translation System the translation phase. The source language is Bicolano and estimating constituent structures from a corpus. An example target language will be Tagalog. of this is the unsupervised probabilistic approaches [29]. Their method in creating their translation was done with a II. RELATED WORKS whole engine that includes the analysis to f- structure, Machine Translation (MT) is defined as “translation from transfer of source to target f-structure, and generation from one natural language (source language (SL)) to another f-structure. Initial linguistic resources were established to test language (target language (TL)) using computerized systems the engine and to develop the full bidirectional and, with or without human assistance” [18]. Different English-Filipino machine translator system. These linguistic applications can benefit in translations [19][20], thus, NLP resources include the formal grammar rules for the English researchers devote their time towards creation of a good and Filipino language, mono-lingual dictionaries for both translator. These were done in application to different languages and the transfer dictionaries, which include language since each language has their own unique structure. transfer rules (structural level) and transfer dictionary (word In 2005, a Hindi to English translation system was develop level). Testing involved subjecting the system to different that focused on designing a system that translate the sentences and sentence constructions in both languages document from Hindi to English using transfer-based (English and Filipino). Results show that translation quality approach. This system takes an input text check its structure is extremely dependent on the available linguistic resources through parsing. Reordering rules are used to generate the [29]. This result shows that translation from Tagalog to text in target language. It is better than Corpus Based MTS English is possible. because Corpus Based MTS require large amount of word As stated about the demographic description of the aligned data for translation that is not available for many Philippines, this causes the country to have many dialects and languages while Transfer Based MTS requires only the communication sometimes become a problem. knowledge of both the languages (source language and target Researches in the field of Natural Language Processing language) to make transfer rules [21]. Another research in (NLP) are trying to solve this by developing a translator that this language translation was developed and experimented on can be used not only for English to Tagalog but Tagalog to solving the word ambiguities to get a high accuracy in the different dialects [30][31][32]. A unidirectional machine translation [22]. This study also uses a parsing technique for translator for languages Tagalog and Cebuano, dialect the development of their system. It is known that a good translator was studied and developed years ago [8][33]. This parsing technique is helpful in the development of a translator research of Yara made used of morphological analysis that is [23][24][25]. based on TagSA (Tagalog Stemming Algorithm) and is Another study on MT focuses on Telugu and Tamil, major focused on an affix correspondence-based POS Dravidian languages with rich literary tradition sharing (part-of-speech) tagger. The rules used in morphological indubitable linguistic similarities and dissimilarities were synthesis are reverse of the rules used in morphological created. An MT between them may be viewed as a bridge to analysis. A bilingual dictionary from Tagalog to Cebuano has understand and share the richness of both the languages. This been developed and is used by the different components of study allows the researchers to look for the features needed to the system. The machine translator has been evaluated, with consider in a rich language since Tagalog belongs to this the Book of Genesis as input, using GTM (General Text classification. The Telugu-Tamil
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