The Impact of Translation Techniques on the Accuracy of the Translation of Commissive Speech Acts’ Responses in Dark Matter Novel
Bibliographic Data
| ID | 21707896 |
|---|---|
| Authors | Dewi Santika (Sebelas Maret University), Mangatur Nababan (0000-0001-9848-4696), M R Nababan (0000-0002-1913-6114, Sebelas Maret University), Djatmika Djatmika (Sebelas Maret University), Djatmika |
| Year | 2020 |
| Volume | 19 |
| Issue | 1 |
| Pages | 1 |
| Publication date | 2020-03-10 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Humanus (JOURNAL) |
| Journal identifiers | ISSN: 1410-8062 • E-ISSN: 2528-3936 |
| Publisher | Universitas Negeri Padang (PUBLISHER • ID) |
| DOI | 10.24036/humanus.v19i1.102684 |
| OpenAlex | W3043294134 |
| Language | EN |
| Citations received | 2 |
| References cited | 2 |
This study was conducted to describe the translation techniques’ impact on the accuracy of the translation of commissive speech act’s responses. The study used descriptive-qualitative research method. The source data used is a novel entitled Dark Matter. The primary data were translation techniques used to translating sentences represented commissive speech acts’ responses. Meanwhile, the secondary data were the quality of the translation of commissive speech act’s responses. The data were collected using content analysis and FGD or Focused Group Discussion through questionnaire and discussion. The result shows that the translator used 15 kinds of translation techniques with 392 times occurrences to translate 79 responses to commissive speech acts. The findings indicate that the use of certain techniques reduce the value of the translation accuracy. Those techniques are literal, discursive creation and addition. The application of the techniques generates the imperfect quality in terms of accuracy that is 2.96
Linguistics · Natural language processing · Pragmatics · Computer Science · Language Acquisition and Education · Artificial Intelligence
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2020 - 2022 (3) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |