Machine translation of embedded clauses (English >French): A comparative analysis of google and Yahoo Babelfish translates
Ramndani, Aziz O
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The main purpose of this study was to investigate whether machine translation (with specific reference to Google Translate and Yahoo! Babelfish Translate) is able to translate embedded clauses from English to French accurately and also to establish whether statistical machine translation (SMT) renders a more accurate translation compared to rule-based machine translation (RBMT). The analysis of the results led to the confirmation of the major hypothesis: machine translation cannot translate embedded clauses accurately since almost 70% of the clauses were mistranslated by each of the MT tools. It also established that omitting the complementizer could be a source of errors in SMT. The study revealed too that changing the complementizer which retain the meaning in the clause i.e. that> which, does not affect the translation and that the fog index is not the main source of errors in machine translation. On the overall, RBMT was found to be more accurate than SMT with its strength lying in the syntactic aspect attributed to the grammatical rules applied in the process of translation. It was also revealed that RBMT is more of a literal translator compared to SMT which is more of a free translator. Chapter one laid the groundwork of the study by anchoring it into the artificial intelligence framework that was intertwined with statistical machine translation (SMT) and rule-based machine translation (RBMT) paradigms. The chapter states, limits and defines the problem that has necessitated the present research. It also establishes the objectives, hypotheses as well as the methodological framework of the research. Chapter two presents the data (embedded clauses) that was collected from grammar books aligned with its corresponding machine translation from Google Translate (SMT). The errors made were analyzed at different linguistic levels i.e. lexical, semantic and syntactic as well as combinations of these errors. Chapter three presents the same data used in chapter two that was collected from grammar books, aligned with its corresponding machine translation from Yahoo! Babelfish Translate (RBMn. The errors made were equally analyzed at different linguistic levels i.e. lexical, semantic and syntactic as well as combinations of these errors. Chapter four deals with a comparative analysis of the translation results from the two paradigms. It explains the errors made with reference to the SMT/RBMT theoretical considerations embedded in the major theoretical framework (artificial intelligence) in order to confirm or disconfirm the hypotheses stated. Finally, the last chapter summarized the whole study, detailed the findings and ended with recommendations.