Translating Causative Have and Get: A Comparative Study between Google Translate Translation and Human Translation

Technological advances have made the ability to translate no longer exclusively belong to humans. Today, machine translation has turned into a tool with superior performance to convert text between languages without the need for human intervention. One of the translation research foci is the studies of causative translation, especially from English to several other languages. Yet, it might be interesting to compare the translation of that topic by human and machine translation. This study investigates the comparison of Google Translate and humans in terms of causative translation from English into Indonesian. The data were obtained from six English novels and their translations in Indonesian. To analyze the data, 100 clauses with causative have and get were selected from English novels and translated by Google Translate into Indonesian. The result showed that the translation and strategies used between Google Translate of causative have and get had similarity with human translation in relation to causative-to-causative translation. Through the investigation, the result is expected to be beneficial for further studies in the translation of causative have and get related to their translations into Indonesian analytic or morphological causative. Furthermore, the result of strategies compared is expected to be beneficial to the translation study regarding machine and human translation in causative, especially from English into Indonesian.

Authors

Institutions

Publication Details

Journal
JET (Journal of English Teaching)
Published
2025-02-19
DOI
https://doi.org/10.33541/jet.v11i1.6652
Primary Topic
Translation Studies and Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Translating Causative Have and Get: A Comparative Study between Google Translate Translation and Human Translation

Daniel Sbastian, Gloria Pratiwi Waang
JET (Journal of English Teaching)
Translation Studies and Practices
article

Translating Causative Have and Get: A Comparative Study between Google Translate Translation and Human Translation

Daniel Sbastian, Gloria Pratiwi Waang
article en

Abstract

Technological advances have made the ability to translate no longer exclusively belong to humans. Today, machine translation has turned into a tool with superior performance to convert text between languages without the need for human intervention. One of the translation research foci is the studies of causative translation, especially from English to several other languages. Yet, it might be interesting to compare the translation of that topic by human and machine translation. This study investigates the comparison of Google Translate and humans in terms of causative translation from English into Indonesian. The data were obtained from six English novels and their translations in Indonesian. To analyze the data, 100 clauses with causative have and get were selected from English novels and translated by Google Translate into Indonesian. The result showed that the translation and strategies used between Google Translate of causative have and get had similarity with human translation in relation to causative-to-causative translation. Through the investigation, the result is expected to be beneficial for further studies in the translation of causative have and get related to their translations into Indonesian analytic or morphological causative. Furthermore, the result of strategies compared is expected to be beneficial to the translation study regarding machine and human translation in causative, especially from English into Indonesian.

JET (Journal of English Teaching)Vol. 11(1)
Indonesia International Institute for Life Sciences (ID), International University (KH)
Openalex Percentile: Top 89%
Translation Studies and Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.