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This study aims to examine machine translation research in journals indexed in the Web of Science to find out the research trending issue, hotspot areas of research, and document co-citation analysis. To this end, 541 documents published between 1992 and 2022 were retrieved and analyzed using CiteSpace, and Bibexcel. Many metrics were analyzed such as document co-citation analysis, sources co-citation analyses, authors’ keywords analysis, and Hirsch index. Data were coded and filtered to include research related to machine translation from the perspectives of language and translation studies. We identified 11 clusters that represented the hotspot research during the period of almost three decades of research. We also discovered that a significant focus of research in machine translation centered around enhancing the translation process through the implementation of neural networks integrated with artificial intelligence. Additionally, we observed the incorporation of human post-editing as a means to refine and improve machine-translated outputs. We found that translation studies journals were the most highly co-cited journals and Google translate was the most highly used machine translation. This study highlights the trending issues and hotspots in machine translation research within language and translation studies. The integration of neural networks with artificial intelligence and human post-editing emerged as prominent areas of focus for enhancing translation quality. The findings of the current study inform future research and technological advancements in machine translation, guiding efforts to improve translation processes and outcomes.
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Stored only when both papers are in the corpus — 20 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.