Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations
The rise of the internet has generated a need for fast online translations, which human translators cannot meet. Statistical tools such as Google and Baidu Translate provide automatic translation from one written language to another. This study reports the descriptive comparison of the machine-trans...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2022-12-01
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| Series: | SAGE Open |
| Online Access: | https://doi.org/10.1177/21582440221142210 |
| _version_ | 1811294179022602240 |
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| author | Muhammad Afzaal Muhammad Imran Xiangtao Du Norah Almusharraf |
| author_facet | Muhammad Afzaal Muhammad Imran Xiangtao Du Norah Almusharraf |
| author_sort | Muhammad Afzaal |
| collection | DOAJ |
| description | The rise of the internet has generated a need for fast online translations, which human translators cannot meet. Statistical tools such as Google and Baidu Translate provide automatic translation from one written language to another. This study reports the descriptive comparison of the machine-translation (MT) with human translation (HT), considering the metadiscoursal interactional features. The study uses a parallel corpus consisting of 79 texts translated from Chinese to English by professional human translators and machine translations (Baidu translate & Google translate) and a comparable reference corpus of non-translated English text. The statistical analysis revealed no statistically significant difference between Baidu and Google translate regarding all types of metadiscoursal indicators. However, the findings of this study demonstrate significant disparities in the interactional characteristics of various HT and MT groups. Compared to the metadiscourse features in non-translated English political texts, human translators were found to outperform machine translations in the use of attitude markers. In contrast, the distribution of directives in machine-translated texts is more native-like. In addition, MT and HT have utilized a significantly smaller number of hedges, self-mention, and readers than non-translated texts. Our results indicate that the MT systems, though still calling for further improvement, have shown tremendous growth potential and may complement human translators. |
| first_indexed | 2024-04-13T05:12:37Z |
| format | Article |
| id | doaj.art-2fc6ddca5d394e0cb22ee54a4281a84b |
| institution | Directory Open Access Journal |
| issn | 2158-2440 |
| language | English |
| last_indexed | 2024-04-13T05:12:37Z |
| publishDate | 2022-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | SAGE Open |
| spelling | doaj.art-2fc6ddca5d394e0cb22ee54a4281a84b2022-12-22T03:00:59ZengSAGE PublishingSAGE Open2158-24402022-12-011210.1177/21582440221142210Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human TranslationsMuhammad Afzaal0Muhammad Imran1Xiangtao Du2Norah Almusharraf3Shanghai International Studies University, Shanghai, ChinaUniversity of Sahiwal, PakistanShanghai Jiao Tong University, ChinaPrince Sultan University, Riyadh, Saudi ArabiaThe rise of the internet has generated a need for fast online translations, which human translators cannot meet. Statistical tools such as Google and Baidu Translate provide automatic translation from one written language to another. This study reports the descriptive comparison of the machine-translation (MT) with human translation (HT), considering the metadiscoursal interactional features. The study uses a parallel corpus consisting of 79 texts translated from Chinese to English by professional human translators and machine translations (Baidu translate & Google translate) and a comparable reference corpus of non-translated English text. The statistical analysis revealed no statistically significant difference between Baidu and Google translate regarding all types of metadiscoursal indicators. However, the findings of this study demonstrate significant disparities in the interactional characteristics of various HT and MT groups. Compared to the metadiscourse features in non-translated English political texts, human translators were found to outperform machine translations in the use of attitude markers. In contrast, the distribution of directives in machine-translated texts is more native-like. In addition, MT and HT have utilized a significantly smaller number of hedges, self-mention, and readers than non-translated texts. Our results indicate that the MT systems, though still calling for further improvement, have shown tremendous growth potential and may complement human translators.https://doi.org/10.1177/21582440221142210 |
| spellingShingle | Muhammad Afzaal Muhammad Imran Xiangtao Du Norah Almusharraf Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations SAGE Open |
| title | Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations |
| title_full | Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations |
| title_fullStr | Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations |
| title_full_unstemmed | Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations |
| title_short | Automated and Human Interaction in Written Discourse: A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations |
| title_sort | automated and human interaction in written discourse a contrastive parallel corpus based investigation of metadiscourse features in machine human translations |
| url | https://doi.org/10.1177/21582440221142210 |
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