![]() Google permits corrections to be put forward. Also unlike Google the operators give a damn on improving the system's capabilities. However, recently I have noticed that (at least on the free version) the quality has dropped considerably. But to get the meaning, I'm not going to create an account and give up more data than I do with google translate.Įnglish: DeepL used to be the preferable alternative to Google translate. In fact, where google translate falls short, deepl is better, and - strangely enough - vice versa. Why bother then, when I can still get 5K of text from google translate without all this faff, and frankly, it's good enough. pay nothing, get nothing, you read comments from people who pay for this service and complain about EXACTLY the same issues, i.e. And then, when you think: 'well, you get what you pay for! - i.e. No way to search for solutions online, no support, no responses, no suggestions, nothing. It still didn't work, so I chucked it out, same with android variant. Then it stopped (God knows, perhaps I pressed some obscure key combination by mistake?!) and reinstalling it didn't help. The app DID have some practical use, accessing translation in any program on Windows (likewise, android variant). The desktop app is a jaggernaut, when all it does is repeat what browser access does. There used to be a way to correct (report) those mistranslations (crowdsourcing) but I don't think they register anywhere, seems it's like spitting against the wind (or against the algorithms). deepl (rarely, but still), the results contain some stunning mistranslations, so I wouldn't be using deepl for languages I know absolutely nothing about. Can I be bothered to create an account and then log in, when every now and then all I need is just to get the meaning of more than a couple of paragraphs? Not really, google translate works fine. Sure you can use 5K at a time, but this means giving away your details (create an account, etc.) and being constantly monitored and your input going to God-know-where and to be sold to God-knows-what-carefully-selected-business-partners. Attention-based systems add on the attention scores to the initial representations (filled circles below) in parallel to all the words with the decoder acting similarly.Deepl used to be superior to google translate, but they have contantly chipped away at the character count: first it was 5K, they reduced it to 3K, now reduced it to 1.5K. Previous approaches to machine translation had a decoder create a representation of each word (unfilled circles below) and using the decoder to generate the translated result using that information. This mechanism is applied to translation in the following way. The key difference is that attention-based systems only perform a small number of these computations and feeds the weighted average of the scores to generate a representation of the word in question. While the actual implementation of each of the systems is different, the underlying principle spans from the paper, “ Attention Is All You Need.”įor a given word, attention-based systems quickly compute an “attention score” to model the influence each word has on another. Thus, both systems struggle with sentences like those presented above.īoth Transformer and DeepL overcome this limitation by applying an attention mechanism to model relationships between words in a sentence more efficiently. Although CNNs are less sequential than RNNs, computational steps to establish the meaningful relationships grow with increasing distance. This becomes a bottleneck for the computer running on TPUs and GPUs that utilize parallel computing. Humans can quickly determine that the meaning of “bank” depends on “road” or “river.” Machines using RNNs, however, process the sentence sequentially, reading every word by word, before establishing a relationship between “bank” and “road” or “river.” ![]() “I arrived at the bank after crossing the river.” “I arrived at the bank after crossing the road.” To illustrate, take the following sentences where the word “bank” has a different meaning in context: Still, these systems based on complex architectures involving recurrent neural networks (RNN) and convolutional neural networks (CNN) were computationally expensive and were limited by their sequential nature. Back in December of 2016, the New York Times published an article on the Google Brain team and how neural networks have elevated the accuracy of Google Translate to a human-like level. ![]()
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