http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
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- 주제어 : web-based machine translation (검색결과 5 건)
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Sung-Dong Kim,Namyun Kim 한국인터넷방송통신학회 2020 International Journal of Internet, Broadcasting an Vol.12 No.2
This paper presents an environment for rule-based English-Korean machine translation system, which supports the translation domain adaptation and the continuous translation quality improvement. For the purposes, corpus is essential, from which necessary information for translation will be acquired. The environment consists of a corpus construction part and a translation knowledge extraction part. The corpus construction part crawls news articles from some newspaper sites. The extraction part builds the translation knowledge such as newly-created words, compound words, collocation information, distributional word representations, and so on. For the translation domain adaption, the corpus for the domain should be built and the translation knowledge should be constructed from the corpus. For the continuous improvement, corpus needs to be continuously expanded and the translation knowledge should be enhanced from the expanded corpus. The proposed web-based environment is expected to facilitate the tasks of domain adaptation and translation system improvement.
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타동성 교체 구문을 활용한 구글 번역기의 영한 번역 평가
조수연 ( Sue Yeon Jo ),박경리 ( Kyoung Lee Park ),전종섭 ( Jong Sup Jun ) 한국외국어대학교 언어연구소 2013 언어와 언어학 Vol.0 No.61
With the recent popularity of free online machine translation services, researchers have been interested in the evaluation of machine translation systems. The increasing literature in the field, however, has not answered two important questions: how accurate the online machine translation for English to Korean is; and how the translation quality is determined by linguistic factors like argument structure and transitivity alternations. The purpose of this paper is to investigate linguistic determinants for successful machine translation of English into Korean, and to suggest possible directions for improving the translation quality from the perspective of lexical semantics. For this, we tested the accuracy of Google Translate (=GT) for English to Korean by using 260 sentences produced by Levin``s (1993) English verb classification. The quality of the translation was assessed with reference to Hampshire & Salvia``s (2010) criteria of clarity and fidelity. Results from the cumulative logit multinomial regression analysis showed that the lexical semantic differences between English and Korean caused the failure of translation, and that GT could not translate the conative and middle intransitive constructions in English accurately. In the end, we argue that the quality of machine translation can be improved above traditional non-linguistic approaches by incorporating in-depth linguistic principles.
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박경리,조수연,전종섭 한국외국어대학교 통번역연구소 2013 통번역학연구 Vol.17 No.3
Researchers have reported positive effects of using pivot languages in improving the quality of statistical machine translation for western languages. The goal of this paper is to confirm whether the quality of English-to-Korean machine translation improves by using a pivot language, and to find out the best pivot language for English-to-Korean machine translation. For this, we carried out translation experiments using Google Translate; i.e., we tested the English-to-Korean machine translation of 83 sentences across 35 pivot languages. The test sentences were carefully controlled with reference to Levin's (1993) lexical semantic classification of English verbs. The log-linear regression analysis reveals that the quality of English-to-Korean machine translation improves when Japanese is used as a pivot language. This result is particularly interesting, since both English-Japanese and Korean-Japanese pairs have lower BLEU scores than the Korean-English pair. This suggests that the BLEU scoring system should be reevaluated and revised for future application to machine translation of natural languages.
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MURPHY ODO DENNIS 영상영어교육학회 2020 영상영어교육 (STEM journal) Vol.21 No.2
Literacy demands placed upon learners of English for academic purposes continues to increase with growing online access to scholarly communities from around the world. To gain full access to these resources, English learners need to develop the requisite skills to consume and produce these texts. Therefore, the purpose of this research was to examine whether online machine translation (MT) can support the academic reading and writing development of second language learners. A pre-test/post-test quasi-experimental design was used where the treatment group was provided with opportunities to read academic articles with MT support to establish whether it could improve their academic vocabulary knowledge, written grammar ability and academic writing. Results were that machine translation tools allow learners to scaffold their comprehension of challenging academic texts to improve their L2 written grammar but not support their vocabulary development or overall L2 writing ability. These findings help to establish the value of using machine translation tools to support academic literacy in English by showing that they contribute to gains in L2 written grammar. Academic writing teachers may consider integrating these tools more into their courses to improve their students’ academic literacy and develop their L2 learning autonomy.
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Murphy Odo,Dennis 영상영어교육학회 2020 영상영어교육 (STEM journal) Vol.21 No.2
Literacy demands placed upon learners of English for academic purposes continues to increase with growing online access to scholarly communities from around the world. To gain full access to these resources, English learners need to develop the requisite skills to consume and produce these texts. Therefore, the purpose of this research was to examine whether online machine translation (MT) can support the academic reading and writing development of second language learners. A pre-test/post-test quasiexperimental design was used where the treatment group was provided with opportunities to read academic articles with MT support to establish whether it could improve their academic vocabulary knowledge, written grammar ability and academic writing. Results were that machine translation tools allow learners to scaffold their comprehension of challenging academic texts to improve their L2 written grammar but not support their vocabulary development or overall L2 writing ability. These findings help to establish the value of using machine translation tools to support academic literacy in English by showing that they contribute to gains in L2 written grammar. Academic writing teachers may consider integrating these tools more into their courses to improve their students’ academic literacy and develop their L2 learning autonomy.
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