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Lingustic-Revision.docxSurname 5
Chang Qiu
Linguistic
11/1/2018
Machine and Human Translate
Most of the contents in the article involve a lot of criticism of the activities and services of the google translate compared to the normal conversation done in a face to face communication. Douglas Hofstadter provides a definitive introduction into the modern uses of technology and its consequences and applications. It is a challenge for thinking technology will solve even the simplest of the forms of human existence, such as language and communication. Hofstadter considers Google translations does not have the capabilities of meeting the balance between human communication and the use of language in different contexts.
In the contemporary community, the field of language translation engines is experiencing a gradual improvement with the recent introduction of the deep neural nets. It has been suggested by some observers and professionals that the era of the “Great AI Awakening” is upon us. If such a development was to occur, it would cause a very devastating upheaval in the Douglas Hofstadter life and beliefs. Although he might be fascinated and interested in the concept of machines translators and the ability of machines to translate, he is still not interested in seeing the human translators being replaced by the inanimate machines.
The idea and belief that this might come, frightens him. To his belief, the concept of human translators should be given more credit and awareness mainly because he considers it to be a form of art which is derived from individuals with many years of language experience throughout their lifetime. If it were to occur that the human translators are to be replaced by the machine translators and become considered as past relics, Douglas Hofstadter would be very saddened and be left in a state of terrible confusion.
There are different issues highlighted by Hofstadter and these issues are at the inability to effectively translate human communication by a machine’s translation configurations. For example, one phenomenon provided in the translation of the statement "Dans Leur Maison, tout vient en paires. Il y a sa Voiture et sa voiture, ses serviettes et ses serviettes, sa bibliothèque et Les siennes” by the google translate. The translation fails due to the different acts and why it affects the consistency and credibility brought by the Google translation.
The translation of any language with the use of a machine translator also contains its own sets of misgivings and challenges. For example, in the case of the French translation, it has been noted that the conversion for French is one of the youngest enterprises as compared to the rest of the other translated language. However impressive the current progress of the endeavor has produced over the past couple of years, the language still continues to face several challenges and issues during their translation process. A recent examination was conducted, trying to identify the problems and challenges faced when converting some languages and his finding are categorized into two well-defined sub problems.
The first being the idea of text analysis, also known as linguistic analysis. It is in this stage that the activities of converting the input text, through its representation as a string of characters into its final linguistic representation. Where the linguistic representation contains and exhibits a complex structure which includes additional information on the grammatical categories of words, prosodic phrase information, tonal or accentual properties of words and finally word pronunciation. Also in the translation of French, another arising issue is the translation of the words for “her” and “his” mainly because they don’t agree with the gender of the possessor but only with the item being possessed.
For example, one phenomenon provided in the translation of the statement "Dans Leur Maison, tout vient en paires. Il y a sa Voiture et sa voiture, ses serviettes et ses serviettes, sa bibliothèque et Les siennes” by the google translate. The program was identified to not being able to realize that the writer was describing a couple, as would have been identified by any human reader, where it was stressed that for each item she had, he had a similar one. Even with the input sentences trying to scream and indicate to the owners of the genders, the translating machine happened to have completely ignored the indications being provided and made everything to be masculine.
Hofstadter indicates the main difference in the above translation between the machine translators and the human translators is that as for humans, they are aware of all the different sorts of concepts about couples such as their pride, jealousy, personal possessions, and privacy, was able to help the human translators be able to detect the quirks of a married couple having their towels to be embroidered "hers" and "his" while as for the case of google translation, as an example of a machine translator, the machine was not familiar with such situations to be able to deduce the same facts during the translation.
It has been identified as difficult for humans coupled with their lifetime of experience and fundamental understanding and the use of the words happens to be a meaningful way of realizing how devoid of all the data and information provided and displayed onto the screen by the machine translators such as the google translation. It is also quite impossible for individuals to presume and believe that a simple piece of software that fluently deals with words may eventually be able to understand and know of their meaning. This classic illusion having being associated with artificial intelligence programs is what is identified as the “Eliza effect.” This is since the first program to pull the belief and idea over people with its seeming understanding of the English language, back in the period 1960 was as a result of the phrase manipulator commonly identified as Eliza.
References
Hofstadter, D. (2018). The Shallowness of Google Translate. The Atlantic. Retrieved from: https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/
