隨着大數据時代的到來, 机器飜譯作爲人工智能領域的一個重要分支正在受到越來越多的關注。對國際机器飜譯進行文獻計量硏究有助于學者更好地把握机器飜譯的發展動態, 發現當前机器飜譯硏究的不足之處。本硏究以CiteSpace V爲工具, 運用期刊雙圖疊加圖譜、作者-机構-國家合作圖譜, 文獻共被引聚類圖譜系統地分析了1993-2017年經驗主義時期間國際机器飜譯領域的硏究狀況, 熱点主題以及前沿趨勢等問題。經硏究發現國際机器飜譯領域的熱点依次爲僅依靠統計學原理的机器飜譯, 綜合運用語言學信息的統計机器飜譯, 領域自适應, 神經机器飜譯, 硏究前沿爲低資源語言的飜譯, 譯后編輯, 質量評估標准。最后, 當前國際机器飜譯領域在旣有机器飜譯技術應用模式的開發, 認知語言學等先驗知識與机器飜譯技術的融合兩個方面有待進一步加强。

With the advent of the age of big data, machine translation has been receiving more and more attention as an important branch of artificial intelligence. The bibliometric study of international machine translation can help domestic scholars to better grasp the development of machine translation and find out the inadequacies of current machine translation research. This paper uses CiteSpace V as a tool to systematically analyze the status, hotspots and frontier trends of research in international machine translation from 1993 to 2017 by using the map of dual-map overlays of journal, the cooperation maps of authors-institutions-country, and the clustering map of the co-citation literatures. In the field of international machine translation, chronological order of the hotspots is machine translation based on statistical principle only, statistical machine translation using linguistic information, domain adaptation, neural machine translation, research frontier includes translation of low-resource language, post-editing, quality estimation of machine translation. This paper holds that the field of machine translation at the moment needs further strengthening in the development of the application of the existing machine translation technology, the fusion of cognitive linguistics and machine translation technology.