Abstract:The present study explored the types of errors found in Google Translate (GT) Chinese-to-English translations and, based on those error types, proposes strategies for optimizing the performance of GT. Seven abstracts written in both Chinese and English from seven articles published in
English Teaching and Learning
in 2017 were selected as the materials. The researchers compared the GT translations to the English abstracts written by the original author(s) and analyzed the problems in the… Show more
“…With the currently available machine translation technology, several methods can be employed to decrease the chance of errors, mostly requiring additional human input to assess the translation systems output. 37,38 Machine translation combined with the utilisation of the review team's knowledge may reduce the financial and resource burden of translation. The combination of both methods might reduce the inaccuracy of machine translation through manual checking while reducing the individual time burden on authors performing full translations.…”
Section: Discussionmentioning
confidence: 99%
“…However, more testing and development is needed before it will be able to be widely utilised. With the currently available machine translation technology, several methods can be employed to decrease the chance of errors, mostly requiring additional human input to assess the translation systems output 37,38 . Machine translation combined with the utilisation of the review team's knowledge may reduce the financial and resource burden of translation.…”
Systematic reviews and maps are considered a reliable form of research evidence, but often neglect non‐English‐language literature, which can be a source of important evidence. To understand the barriers that might limit authors' ability or intent to find and include non‐English‐language literature, we assessed factors that may predict the inclusion of non‐English‐language literature in ecological systematic reviews and maps, as well as the review authors' perspectives. We assessed systematic reviews and maps published in Environmental Evidence (n = 72). We also surveyed authors from each paper (n = 32 responses), gathering information on the barriers to the inclusion of non‐English language literature. 44% of the reviewed papers (32/72) excluded non‐English literature from their searches and inclusions. Commonly cited reasons included constraints related to resources and time. Regression analysis revealed that reviews with larger author teams, authors from diverse countries, especially those with non‐English primary languages, and teams with multilingual capabilities searched in a significantly greater number of non‐English languages. Our survey exposed limited language diversity within the review teams and inadequate funding as the principal barriers to incorporating non‐English language literature. To improve language inclusion and reduce bias in systematic reviews and maps, our study suggests increasing language diversity within review teams. Combining machine translation with language skills can alleviate the financial and resource burdens of translation. Funding applications could also include translation costs. Additionally, establishing language exchange systems would enable access to information in more languages. Further studies investigating language inclusion in other journals would strengthen these conclusions.
“…With the currently available machine translation technology, several methods can be employed to decrease the chance of errors, mostly requiring additional human input to assess the translation systems output. 37,38 Machine translation combined with the utilisation of the review team's knowledge may reduce the financial and resource burden of translation. The combination of both methods might reduce the inaccuracy of machine translation through manual checking while reducing the individual time burden on authors performing full translations.…”
Section: Discussionmentioning
confidence: 99%
“…However, more testing and development is needed before it will be able to be widely utilised. With the currently available machine translation technology, several methods can be employed to decrease the chance of errors, mostly requiring additional human input to assess the translation systems output 37,38 . Machine translation combined with the utilisation of the review team's knowledge may reduce the financial and resource burden of translation.…”
Systematic reviews and maps are considered a reliable form of research evidence, but often neglect non‐English‐language literature, which can be a source of important evidence. To understand the barriers that might limit authors' ability or intent to find and include non‐English‐language literature, we assessed factors that may predict the inclusion of non‐English‐language literature in ecological systematic reviews and maps, as well as the review authors' perspectives. We assessed systematic reviews and maps published in Environmental Evidence (n = 72). We also surveyed authors from each paper (n = 32 responses), gathering information on the barriers to the inclusion of non‐English language literature. 44% of the reviewed papers (32/72) excluded non‐English literature from their searches and inclusions. Commonly cited reasons included constraints related to resources and time. Regression analysis revealed that reviews with larger author teams, authors from diverse countries, especially those with non‐English primary languages, and teams with multilingual capabilities searched in a significantly greater number of non‐English languages. Our survey exposed limited language diversity within the review teams and inadequate funding as the principal barriers to incorporating non‐English language literature. To improve language inclusion and reduce bias in systematic reviews and maps, our study suggests increasing language diversity within review teams. Combining machine translation with language skills can alleviate the financial and resource burdens of translation. Funding applications could also include translation costs. Additionally, establishing language exchange systems would enable access to information in more languages. Further studies investigating language inclusion in other journals would strengthen these conclusions.
“…This is caused by the factor of writing, which is the most complex skill in language, so it has the highest difficulty level. In the last ten years, several studies on writing have shown that writing strategies are essential in efforts to improve writing skills [4], [5], writing strategies [6], [7], [8], [9], [ 10], and writing motivation [11], [12], [13] which will influence other skill contexts, for example, reading or cognitive abilities. Writing is not easy because it requires complex skills and tenacity from the writer [14].…”
The study of contextual writing is currently the concern of various parties, one of which is related to the writing community. In this regard, this article aims to explore the Indonesian blind writing community. The method used in this study is qualitative because the researcher focuses more on narrative data. Respondents in this study amounted to 101 respondents who are members of the Indonesian blind writing community, with a distribution of 45.5% male and 55.5% female. Data collection techniques were carried out using two models, namely interviews and questionnaires. Data analysis techniques used identification, classification, reduction, exposure, verification, and finalization stages. The research results show that first, the community of blind writers is a community of blind writers that is still new but has members spread across various regions, starting from East Java,
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