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The aim of this study is to help English-based technicians produce high-quality translated documents with the help of free software tools (such as machine translation+post-translation compilation technology, computer-aided translation technology, etc.). This paper discusses a method of iteratively rewriting technical documents to help technicians generate more accurate bilingual parallel documents. It also studies how to use control language rules to rewrite technical documents to eliminate ambiguity and sentence complexity in order to improve the translatability of documents. Finally, a method is proposed to help users choose the most suitable machine translation engine. Introduction With the development of economic globalization, there is a growing demand for translated documents within multinational corporations. Enterprise technicians require bilingual parallel documentation of product specifications or help documents (Lemair, 2016). Although company technicians usually have a certain foundation in English, if they use some free translation software tools to translate, when the enterprise does not use the same terminology management for documents, there is no version control and revision records, or even lack of necessary revision, the quality of documents translated by technicians is often very poor. These inaccurate translated documents often have potential disastrous consequences. In order to save the cost of products, companies are often reluctant to pay the corresponding fees to entrust professional translators to do these tasks. They are also reluctant to train relevant technical translators to use memory translation software, such as SDL-Trados, Qmem, etc., although some software is free (OmegaT, SmartCAT, Poedit, MateCat, etc.). How to use the existing free software tools to help technicians produce high quality bilingual documents is an urgent problem for enterprises[1,2]. Controlling Language Due to the arbitrariness of natural language (NL), ambiguity often occurs in communication. To solve this problem, language engineers have created controlled language (CL). Controlled language is defined as "artificially defined and belongs to a subset of natural language (usually English). Controlled language is restricted in terms of words, sentences and semantics." Compiling the source language according to CL rules before machine translation can make the source language text more suitable for MT system analysis and understanding. These features are often referred to as "Underwood, Jongejan 2001" or, more accurately, "Negative Translatability Index" (NTIs) (O'Brien 2005). Relevant studies have also confirmed that the use of CL rules can have a positive impact on MT output (Aikawa et al 2007; Bernth 1998; Mitamura and Nyberg 1995; Mitamura 1999). One of the objectives of this study is to summarize a set of language control rules, which can be used to retell or edit the source language sentences before translation, reduce ambiguity and improve the quality of machine translation[3,4,5]. The steps of designing controlled language rules are as follows: 167 1) Designing a set of controlled language rules suitable for enterprises to help technicians write technical documents with high translatability; the main rules of controlled language can be divided into four big blocks according to the constraints on language: lexical rule subclass, syntactic rule subclass rule, text rule subclass rule and pragmatic rule subclass rule. 2) Prepare a source language single sentence test set rewritten by controlled language rules; 3) Preparing a set of source language single sentence test sets that do not conform to the controlled language rules; 4) Two sets of sentences are processed by machine translation system (one set of controlled language rules and one set of no controlled language rules). Wilcoxon sign rank test is used to test the translation quality of the output target language, as shown in Table 1. Table 1. Comparison of Translation Quality Assessment for Two Kinds of Test Sets. Text numbering Multidimensional grading evaluation Ti Multidimensional grading evaluation Ti’ error Grade

Research on High Quality Bilingual Parallel Documents of Science and Technology Produced by Application Software Tools and Control Language