Automated and Human Interaction in Written Discourse
A Contrastive Parallel Corpus-based Investigation of Metadiscourse Features in Machine-Human Translations
Bibliographic Data
| ID | 3586578 |
|---|---|
| Authors | Muhammad Afzaal (0000-0003-4649-781X, Shanghai International Studies University), M Imran (0000-0002-8754-2157, Prince Sultan University, corresponding author), Xiangtao Du (Shanghai Jiao Tong University), Norah Almusharraf (0000-0002-6362-4502, Prince Sultan University) |
| Year | 2022 |
| Volume | 12 |
| Issue | 4 |
| Publication date | 2022-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/21582440221142210 |
| OpenAlex | W4312338678 |
| Language | EN |
| Citations received | 16 |
| References cited | 32 |
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
Corpus linguistics · Linguistics · Machine translation · Metadiscourse · Natural language processing · The Internet · World Wide Web · Computer Science · Discourse Analysis in Language Studies · Language, Metaphor, and Cognition · Topic Modeling · Artificial Intelligence
Effects of pre-editing operations on audiovisual translation using TRADOS
Examining the Cultural Connotations in Human and Machine Translations
Analyzing the role of ChatGPT as a writing assistant at higher education level
A corpus-based comparison of linguistic markers of stance and genre in the academic writing of novice and advanced engineering learners
Emerging E-learning trends
Investigating discourse markers “you know” and “I mean” in mediatized English political interviews
Enhancing Academic Writing in a Linguistics Course with Generative AI
Syntactic complexity in translated eHealth discourse of Covid-19
A comprehensive bibliometric analysis of speech acts in international journals (2013–2023)
The persuasive strategies in more and less prestigious linguistics journals
Bridging cultures through explicitation
A corpus-based study of English editorials
The impact of AI applications on pre-service teachers' public speaking anxiety and academic speaking skill in the context of oral presentations
A corpus-based study of stance markers in academic thesis writing by L2 undergraduates
Persuading in Arabic and English
When Student Translators Meet With Machine Translation
The Writing Scholar
Bringing in the Reader
“In this paper we suggest”
Interactions in L1 and L2 undergraduate student writing
Qualification and certainty in L1 and L2 students' writing
Metadiscourse in Persuasive Writing
Disciplinary interactions
Interaction in academic writing
Do adult ESL learners’ and their teachers’ goals for improving grammar in writing correspond
Metadiscourse in Chinese and American graduate dissertation introductions
Same Source, Different Outcomes
Genre Analysis
Persuasion and context
Hedging and boosting in abstracts of applied linguistics articles
Metadiscourse
Metadiscourse
Meta-Talk
| Unique citing works | 16 |
|---|---|
| Citations per year | 5,33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 14 |