Although Machine Translation engines are a good tool, they are unable to break down all the language barriers. The learning algorithms behind machine translation are called artificial intelligence, but machines are not intelligent in the same way as humans are. Artificial intelligence has become a buzzword. All AI are capable of recognizing patterns, learning and making decisions. But is this really intelligence? The definition according to the dictionary is that intelligence is the ability of humans to think abstractly and rationally and derive useful action from such thoughts. This is why people are able to create new things. This involves a little more than only recognizing patterns and connections, learn rules and applying them. It involves creativity and emotions. This is where AI systems fail. They cannot think and are therefore by nature not creative. At best, AI systems simulate a part of our intelligence.
The learning algorithms behind machine translation make the system learn about the context in which the words are used.Surprisingly good results have been achieved and the quality of MT has improved enormously at a fast pace the past years.
At the moment MT is widely adopted by translators. MT can boost productivity. If the translation provided by the machine is good enough so it can be used for post-editing, it can be used as a basis. This makes it possible to translate more content faster, but be careful, the devil can be in the details.
The drawbacks of Machine Translation
Machine translation is not suitable for all text types. Factal content can be handled well by MT because of its short sentences, uncomplicated language and unambiguous terminology. On the other hand, as soon as a text starts to describe meaning between the lines MT cannot perform well. Machine Translation engines know how to process words, not understanding a wider context or hidden meaning. Multiple meanings of words remain a constant struggle. A human translator will always be necessary to convert the meaning of the message.
What will the future bring
According to Adam Bittlingmayer from ModelFront in the coming years the focus will be more on controlling and checking mechanisms. Bittlingmayer explains: “Neural approaches also introduce new problems for text generation like translation. So I think we will see focus on quality estimation , confidence and risk. That’s the obvious way to fail less on semantic ambiguities and the anaphora resolution”.