This study approaches the investigation of the simplification hypotheses in corpus-based translation studies from a syntactic complexity perspective. The research is based on two comparable corpora, the English monolingual part of COCE (Corpus of Chinese-English) and the native English corpus of FLOB (Freiburg-LOB Corpus of British English). Using the 13 syntactic complexity measures falling into five subconstructs (i.e. length of production unit, amount of subordination, amount of coordination, phrasal complexity and overall sentence complexity), our results show that translation as a whole is less complex compared to non-translation, reflected most prominently in the amount of subordination and overall sentence complexity. Further pairwise comparison of the four subgenres of the corpora shows mixed results. Specifically, the translated news is homogenous to native news as evidenced by the complexity measures; the translated genres of general prose and academic writing are less complex compared to their native counterparts while translated fiction is more complex than non-translated fiction. It was found that mean sentence length always produced a significant effect on syntactic complexity, with higher syntactic complexity for longer sentence lengths in both corpora. ANOVA test shows a highly significant main effect of translation status, with higher syntactic complexity in the non-translated texts (FLOB) than the translated texts (COCE), which provides support for the simplification hypothesis in translation. It is also found that, apart from translation status, genre is an important variable in affecting the complexity level of translated texts. Our study offers new insights into the investigation of simplification hypothesis from the perspective of translation from English into Chinese.

Artificial Intelligence (AI) has garnered considerable attention from educators, policymakers, and scholars within the realms of education and social sciences, achieving notable historical significance. Employing bibliometric analysis, this paper aims to provide a comprehensive analysis of the most noticeable trend of using AI in education. The corpus comprises a dataset of publications sourced from Web of Science (WoS) and Scopus-indexed journals. It concentrates on contemporary scientific research and innovative methodologies within the education sector, particularly relevant to the intersection of education and technology in the AI era. The results reveal a significant rise in AI-related technological research beginning in 2018, with citations reaching their zenith in 2019. Furthermore, collaborative metrics indicate that the United States and China are leading in publication volume. In addition, the study highlights a rising interest in AI applications within education and healthcare, particularly in the context of the COVID-19 pandemic.

The paper examines the construction of China–Pakistan Economic Corridor (CPEC) through corpus-based critical discourse analysis of news in the Pakistani press. Overall, Pakistani newspapers show China as a slogan of “brotherhood” and a stereotype of China’s relationship as an “ironic bond” between China and Pakistan. The analysis reveals that a bulk of friendly and positive discourses about China have been constructed in the Pakistani media reports. The role of discourses pertinent to the ideological construction of image of China’s mega economic project Belt and Road Initiative (BRI), and CPEC is discussed. However, a deeper insight into the lexical choices of newspapers also suggests that plenty of intricacies and sensitivities are existent in issues pertaining to CPEC and BRI.

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.

The investigation of learners’ interlanguage could greatly contribute to the teaching of English as a foreign language and the development of teaching materials. The present study investigates the collocational profiles of large-scale written production by English learners with varied L1 backgrounds and different proficiency levels. Using the British National Corpus as reference corpus, learners’ collocation use was extracted by corpus query language and further identified by t-score via Python programming language. The collocation list consists of 2,501 make/take + noun (the direct object) collocations. Findings show that proficient learners tend to use collocations containing more semantically complicated and abstract noun elements for varied communication tasks. Moreover, advanced learners are inclined to use collocations comprised of more difficult and longer noun elements.

Pakistan’s geographical location is ideal for China-Pakistan Economic Corridor (CPEC) Project, and it formally recognizes as an iconic model of Belt and Road Initiative (BRI) plan. This corridor is laid at the cross intersection of three regions of economic growth include: China, Central Asian Republics and South Asia. It plays a vital role of inter-connectivity between these three regions, where three billions of inhabitants live, which is roughly half of the world population. The central argument of the paper is sternly conceptual and based on the subsequent arguments: foremost, the foundation of CPEC project is a green signal for the regional connectivity, mutual cooperation, economic growth and stability of the entire region of South and Central Asia. Secondly, the topographical position of Pakistan has an incredible potential for corridor development and manipulation process. Thirdly, due to the iconic model of belt and road initiative plan, the CPEC corridor has broad potential for regional economic growth because of shared boundaries and cross intersection position with China, CARs and South Asia. Finally, with the copious fruitful opportunities, this project facades somber challenges internally and outwardly. Hence, this study uses a positive-sum logic to analyze central argument and also discusses the policy inputs that may be prolific for regional growth and reforms of the project.