23

Who claimed that machine translation is as good as a human translator? For me, as a professional translator who makes his living on translation for 35 years now, MT means that my daily production of human quality translation has grown by factor 3 to 5, depending on complexity of the source text. I cannot agree that the quality of MT goes down with the ...


7

Google's translations can be useful, especially if you know that the translations are not perfect and if you just want to have an initial idea of the meaning of the text (whose Google's translations can sometimes be quite misleading or incorrect). I wouldn't recommend Google's translate (or any other non-human translator) to perform a serious translation, ...


6

You have asked quite a lot of questions, some of which cannot be answered definitively . To give an insight of the quality (and its history) of machine translations I like to refer to Christopher Manning his 'one sentence benchmark' as presented in his lecture. It contains one Chinese to English example which is compared with Google Translate output. The ...


5

It's not quite clear what you are asking. So I'll answer in separate parts. Why is the translation different from the official title? It could be simply because machine translation is not perfect, or our human translator took some creative liberties when translating. In this case it seems to be both. Note that 龍爭虎鬥 properly translated doesn't mean ...


4

Google has achieved significant progress in AI translation, but it's still no-where near a qualified human translator. Natural language translation is already very challenging, adding domain knowledge to the equation is too much even for Google. I don't think we have the technology to translate an arbitrary book from one language to another reliably.


4

I don't know what model Google is using for their translations, but it's highly likely that they're using one of today's SOTA deep learning models. The latest NLP models are trained on data scraped from the web, e.g. OpenAI's GPT-2 was trained on a dataset of 8 million web pages, Google's BERT was trained on the BookCorpus (800M words) and English Wikipedia (...


4

It really depends on the language pair and the topic of the content. Translating to/from English to any other language usually is the best supported. Translating to and from popular languages works better, for example, translating from English to Romanian is a poorer translation than English to Russian. But translating from English to Russian or Romanian is ...


3

Usually, in natural language processing (NLP), they are using Sequence to Sequence Learning (Seq2Seq) with Neural Networks, such as Recurrent Neural Networks or more recently the Transformer (you can find two very good papers here, and here). During training, to ensure the same size of the input and output they can just search for the longest sentence they ...


2

Am I wrong and Google's translations are nevertheless readable, helpful and useful for a majority of users? Yes, they are somewhat helpful and allow you to translate faster. Or does Google have reasons to retain its greatest achievements (and not to show to the users the best they can show)? Maybe, I don't know. If you search for info, Google does ...


2

This is an good question and I have to wonder if someday we might. It may simply be a matter of formalizing all of the concepts conveyed by humans, which is emergent, but has to be finite. The present algorithms do not understand the content in a human sense of meaning, but are refining a statistical model to continually produce more accurate output. You ...


2

Apologies for not writing in English. Please find the adapted translation here: To give interested people an idea of the quality of MT (DeepL) please see this example from a text I was working on this morning (6,300 words, started at 9 am, delivery today around 1 pm and still find time for this post). I was working on this sentence (201 words) when I ...


2

As you know google translation works base on statistical methods. In statistical translation, many parameters can be related to the final result. One of these parameters is co-occurrence of words in a sentence. Hence, as this translator learn languages from different utterances by human and pre-written translations, and different parameters in the text are ...


1

Simple old Latin is different from Latin and in the language words are added to written language that are not spoken as well as reverse order of words to have forward meaning.


1

Surprisingly all the other answers are very vague and try to approach this from the human translator POV. Let's switch over to ML engineer. When creating a translation tool, one of the first questions that we should consider is "How do we measure that our tool works?". Which is essentially what the OP is asking. Now this is not an easy task (some other ...


1

This will be not so much an answer as a commentary. The quality depends on several things, including (as Aaron said above) 1) the language pair and 2) the topic, but also 3) the genera and 4) the style of the original, and 5) the amount of parallel text you have to train the MT system. To set the stage, virtually all MT these days is based off of parallel ...


1

Google uses user input to improve translation. Some user may have provided an input to the Traditional Chinese characters using English characters only instead of pinyin, which would introduce a mistake into the data used by the translator. Since the model is statistics based, such a mistaken translation can only be assigned a lower but non-zero probability ...


1

Chinese words 'fu' means with different intonation marks either happiness, huspand or tiger. Without correct intonation notation in English it may translate in Chinese as Tiger. Movie title has Chinese 'happiness' character, but Google mixes it as Tiger.


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