雅思考试主要是通过对考生听、说、读、写四个方面英语能力的考核，综合测评考生的英语沟通运用能力，实现“沟通为本”的考试理念。对于雅思考生来说，也有很多考试难点和政策盲区需要帮助解答。今天雅思无忧网小编准备了雅思作文范文:机器翻译与人工翻译，希望通过文章来解决雅思考生这方面的疑难问题，敬请关注。
雅思考试中写作部分的考察很重要，又很难在短时间内提分，就更需要大家用功在平时。下面是听力课堂小编整理的雅思作文范文:机器翻译与人工翻译的资料，平时多看多练，考试正常发挥就好。
Write about the following topic:
Machine translation (MT) is slower and less accurate than human translation
and there is no immediate or predictable likelihood of machines taking over this
role from humans.
Do you agree or disagree?
Model Answer:
It is true that there have been great advances in technology over the last
forty years. For example / For instance the use of mobile phones and e-mail
communication are common these days. However, machines that translate from one
language to another are still in their early stages.
It seems to me that a machine could never do as good a job as a human,
especially when it comes to interpreting what people are saying. Of course,
machines can translate statements such as “Where is the bank?” but even simple
statements are not always straightforward because the meaning depends on more
than just words. For instance / For example the word “bank” has a number of
different meanings in English. How does a translating machine know which meaning
to take?
In order to understand what people are saying, you need to take into
account the relationship between the speakers and their situation. A machine
cannot tell the difference between the English expression “Look out!” meaning
“Be careful!” and “Look out!” meaning “Put your head out of the window”. You
need a human being to interpret the situation.
Similarly with written language, it is difficult for a machine to know how
to translate accurately because we rarely translate every word. On the contrary,
we try to take into consideration how the idea would be expressed in the other
language. This is hard to do because every language has its own way of doing and
saying things.
For these reasons I feel that it is most unlikely that machines will take
the place of humans in the field of translating and interpreting. If machines
ever learn to think, perhaps then they will be in a position to take on this
role.
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