Using Grammatical and Semantic Correction Model to Improve C hinese-to- T aiwanese Machine Translation Fluency (2022)

Abstract: Currently, there are three major issues to tackle in Chinese-to-Taiwanese machine translation: multi-pronunciation Taiwanese words, unknown words, and Chinese-to-Taiwanese grammatical and semantic transformation. Recent studies have mostly focused on the issues of multi-pronunciation Taiwanese words and unknown words, while very few research papers focus on grammatical and semantic transformation. However, there exist grammatical rules exclusive to Taiwanese that, if not translated properly, would cause the result to feel unnatural to native speakers and potentially twist the original meaning of the sentence, even with the right words and pronunciations. Therefore, this study collects and organizes a few common Taiwanese sentence structures and grammar rules, then creates a grammar and semantic correction model for Chinese-to-Taiwanese machine translation, which would detect and correct grammatical and semantic discrepancies between the two languages, thus improving translation fluency.

## introduction machine translation systems are being increasingly used across multiple fields, such as businesses, tourism and medical industries. these systems can translate multiple languages like english, chinese, japanese and even obscure traditional languages such as swahili and croatian. taiwanese machine translation is likewise gaining importance as the government becomes more aware of its historical and cultural importance, thus setting out to digitally preserve the language. there has been multiple research papers on chinese-to-taiwanese (henceforth referred to as c2t) machine translation, usually focusing on the issues of multi-pronunciation words and unknown words translation. however, despite the importance of grammatical and semantic differences between languages in machine translation, it has been observed that papers that integrate them into c2t translation systems are comparatively rare, which results in the output of c2t systems losing fluency and potentially the original meanings of the original chinese input. as such, this paper proposes a grammatical and semantic error detection and correction model, which can improve c2t translation fluency by correcting the discrepancies between chinese and taiwanese grammar. ## related work considering the importance of machine translation @xcite @xcite , researchers begin to apply rule-based @xcite , statistical @xcite , and neural machine translation @xcite technology to various languages, including c2t translation models @xcite , in order to tackle various recurring issues in this field, such as choosing the correct pronunciation for multi-pronunciation words and unknown words. for multi-pronunciation words, @xcite ) extracts the features of each word in the input sentence, such as part-of-speech (pos) and semantic meaning, then employ feature models based on word features of co-occurring words and layered structure to select the most suitable translation rule for the chinese words. for unknown words, @xcite ) uses a mixed model which utilizes the prefix and suffix of words, pronunciation, word subsets, etc. to statistically analyze the corresponding taiwanese pronunciation of unknown words in chinese sentences. @xcite also attempts to resolve insertion/deletion issues in c2t translation by compiling a set of insertion/deletion rules, calculate the confidence scores, then combine naïve-bayes and crf statistical models to perform machine learning, improving the fluency of the taiwanese translation output. @xcite , on the other hand, uses a convolutional neural network (cnn) deep learning model, the c2t-pronunciation parallel corpus icorpus and a precompiled chinese-taiwanese parallel dictionary to create a chinese-character-to-taiwanese-pinyin module and perform whole-sentence translation. however, the paper does not focus on the issues of multi-pronunciation words and unknown words encountered during c2t translation. in comparison to the issues of multipronunciation words and unknown words pronunciation, c2t grammatical and semantic level issues, such as structural transformation or split chinese words, are less commonly explored in these research papers. as such, this paper explores sentence structure transformation based on taiwanese grammar. ## conclusion and future work this paper focuses on correcting grammatical and syntactic differences between chinese and taiwanese encountered during c2t machine translation by building a grammatical and semantic error detection and correction module, which can transform the grammar of the chinese sentence inputs into their corresponding taiwanese sentence structures in accordance the 34th conference on computational linguistics and speech processing (rocling 2022) taipei, taiwan, november 21-22, 2022. the association for computational linguistics and chinese language processing system name wer translation sample sentence 1: 男子酒測值高達0.83毫克，被依公 共危險罪送辦。 translation sample sentence 2: 5000 元的藍牙耳機讓我真的會好 奇，真的有這個價值嗎？ ncku 39.2% lâm-tsú tsiú tshik ta t kuân-kàu x.xx hô-khik ，pī i kong-kiōng guîhiám tsuē sàng pān 。 xxxx guân ê nâ gê hī ki niū guá ūiánn ē-hiáu hònn-kî ，ū-iánn iú tse ê kè-ta t kiám？ ithuan 32.8% lâm-tsú tsiú tshik ta t ko ta t 0.83 hôkhik,pī i kong-kiōng guî-hiám tsuē sàng pān. 5000 guân ê nâ gê hīnn-ki niū guá tsin tik ē hònn-kî,tsin ê ū tsit kò kèta t má? nctu scl 20% tsa1 poo1 tsiu2 tshik4 tat8 kuan5 kau3 phi5 khi3 sam1 ho5 khik4 , pi7 an3 kong1 kiong7 hui5 hiam2 tsue7 sang3 pan7 . experiment proves that a grammatical and semantic error detection and correction module can successfully improve translation fluency of c2t machine translation. the correction module can be widely used in areas that require machine translation, and would greatly contribute to meetings, tourism, language education, elder care, ai, etc. future work would include expanding the dictionary to include more words exclusive to chinese which are found in news articles, such as technical terms, and idioms. in addition, for some chinese words and sentence structures, translating them into taiwanese and transforming the grammatical structure requires deeper knowledge of the original semantic meaning. sometimes the translation result may be structured differently from the original, and this issue would also be explored in the future. lastly, the grammatical transformation rules and words used in this paper may not be commonly used by native taiwanese speakers, since translating from chinese to taiwanese is relatively lax. as such, a field survey may be conducted in the future to collect native speakers'opinions and feedback on the results of this translation system to help it output sentences that better fit the users'daily usage habits.