Activating the popup window is done with CTRL Q.
The default for accessing translations by selecting text is CTRL E. Using the program is mostly completed through the use of hot keys which can be configured in the Options interface.
QTranslate Portable can automatically detect the language you have input into the text box, or if preferred, the language translation pair can be selected manually. QTranslate can also translate text in its dedicated interface and is only limited by the number of languages the online services it relies on support. You can use the application in most Windows applications which allow you to select text while also being able to insert translations quickly through a couple of clicks. QTranslate Portable is a simple program which allows you to translate text between languages by connecting to online services including Google Translate, Bing Translate, Prompt, Babylon and others. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system.A free and light translation app connecting to Google Translate and others. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. This method provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. To improve handling of rare words, we divide words into a limited set of common sub-word units ("wordpieces") for both input and output. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. Also, most NMT systems have difficulty with rare words. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems.
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