Machine translation development can be traced backed to the early 1950s when machine translation researchers
adopted the crude dictionary-based approaches which resulted in just word-for-word translation. Gradually
linguistic theories were added to machine translation. According to Hu (2011), the machine translation methods
can be classified into four kinds: 1) the linguistic approach; 2) the transfer approach; 3) the interlingual approach;
4) the knowledge-based approach. The 1990s witnessed machine translation’s acceleration propelled by the
development of corpus linguistics, which is mainly example-based. Such kind of machine translation
presupposes the collection and storage of exiting correct translations. Many machine translation systems such as
Google Translator and Bing Translator have been developed. Machine translation study has boomed, but there is
great controversy as to the feasibility and capability of machine translation.
It is commonly believed that machine translation is of some help in the translation of some texts. In other words,
machine translation has some limitations. “Many of the problems that machine translation faces cannot be solved
at all. High-quality translation done solely by machines is not possible and machine translated texts will continue
to be plagued by errors in the future, ranging from eccentric turns of phrase to grave distortions of meaning”
(Madsen, 2009, p. 5). Some others also point out the existing problems. “Seen from the angle of industrialization
and application, the society has an urgent demand for more applicable machine translations systems or programs.