Machine translation systems have emerged and have been developed since 1940s, a decade representing a milestone of computing and of machine evolution in human history. No matter which generations of machine translation approaches and paradigms, rare machine translation systems, including those commercial systems, can produce adequate translation “independently” without human intervention if the final product is used for dissemination. Post-editing is one of the important intervention strategies essential to the quality of the output. Research on human translation and machine translation with post-editing has always been one of the popular subjects of research in the machine translation field. Nevertheless, although there have been a number of studies on machine translation with post-editing carried out to date, there is still a lack of concrete data available with regard to the actual productivity and efficacy of postediting of machine translation for the translation direction from English to Chinese. The purpose of the current research is to investigate the efficacy of machine translation with respect to English to Chinese translation, to compare the differences between human translation and machine translation with post-editing and to explore people’s attitudes towards machine translation with post-editing. 25 postgraduate students majoring in Translation Studies from University of Macau were invited to complete an online survey. Half of them were asked to translate one user manual Privacy from scratch and then post-edit the machine translation of another one named Bluetooth whereas for the other half, the user manuals were interchanged in a way that students were asked to translate Bluetooth manually and post-edit the machine translation of Privacy. The data collected was analysed in four aspects, including translation productivity, translation accuracy assessment, respondents’ attitudes towards MT and other observations on respondents’ post-edited outputs. The results indicated that machine translation was helpful in improving most of the students’ translation speed and productivity to a certain extent. In terms of quality, machine translation with post-editing sets made fewer errors and achieved higher accuracy. Besides, respondents’ attitudes towards MT were quite positive and supportive. It is hoped that the present study can offer preliminary insights and observations for further research in this area.