Please make Pull Requests for good resources, or create Issues for any feedback! Seq2Seq solves the traditional fixed-size input problem thatEffective Approaches to Attention-based Neural Machine Translation prevents traditional DNNs from mastering sequence based tasks such as translation and question answering. It has been shown to have state of the art performances in English-French and English-German translations and in responding to short questions. Seq2Seq was first introduced in late 2014 by 2 papers (Sequence to Sequence Learning with Neural Networks and Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation) from Google Brain and Yoshua Bengio's group. The two papers took a similar approach in machine translation, in which Seq2Seq was developed upon.

Machine translation (MT) systems such as Google Translate have become part of our daily life. But how do they work? In this talk, I'll explain how these systems are built. In the first part of my talk, I'll present a general overview of the field and the key ideas driving modern MT systems. In the second part, I'll dig deeper into the statistical techniques used to estimate translation models from data, and discuss some of the current hot topics in the field."

Microsoft's Translator app for Android now has the ability to translate text within images. Its latest update also adds additional downloadable language packs, and an inline translator that makes understanding foreign text in other apps even easier. With the ability to translate text within images, Translator no longer requires you to enter words and phrases manually. You can take a picture of signs, menus, leaflets, emails, and more -- and Translator will find the text within them, then translate it into your native tongue. What's more, Translator supports real-time image translation, which means you don't have to snap a picture first and then translate it; simply point your camera at the subject and it will work its magic instantly.

Torch implementation of a standard sequence-to-sequence model with attention where the encoder-decoder are LSTMs. Also has the option to use characters (instead of input word embeddings) by running a convolutional neural network followed by a highway network over character embeddings to use as inputs. The attention model is from Effective Approaches to Attention-based Neural Machine Translation, Luong et al. We use the global-attention model with the input-feeding approach from the paper. The character model is from Character-Aware Neural Language Models, Kim et al.

Like the iOS version, it also works on saved images, but it should be noted that Windows Phones have had image translation since 2010. This is powered by Microsoft's proprietary Deep Learning engine it uses for Bing's and Skype's translation options, something more advanced than Google Translate's statistical models and crowdsourcing. That said, Google Translate's Android app has had image translation since at least August 2012. So this is nothing really groundbreaking. The Android app also gets Inline Translation, which lets users hover over text phrases to quickly convert them into any of the 50 languages in the app's online library.

Microsoft Translator may be your new best friend for international travel. According to Microsoft, you can point your phone at signs, menus, flyers, or anything else that has text visible and get an instant translation. The feature also works with saved images in your phone's gallery or anywhere else you grab a photo from. Another clever feature is inline translation, which allows you to highlight text from an app, open up the Other Options menu and then choose Translator to get the content translated to one of 52 different languages (this feature requires Android Marshmallow). The new Inline Translation feature will translate text inside of another app.

Where can I find a trained machine translation seq2seq model? Title says it all - I'd like to play around with a well trained LSTM sequence to sequence MT model, but I'd rather not futz around with training one. I am not aware of any that exist. You will have to train one yourself or convince someone else to train one for you. If you just want to push input and get output, you might find this demo from Bengio's lab interesting: http://104.131.78.120/

In this special guest feature, Vasco Pedro, CEO and Co-Founder of Unbabel, discusses the importance of machine translation for natural languages and how it currently lacks the quality companies demand for their content. Dr. Pedro' company is Unbabel, the Y Combinator-backed startup that combines crowdsourced human translation and machine learning to deliver fast translation services to businesses with human tone and nuance. Vasco previously worked for Google helping to develop technology for data computation and language at scale, and served as a research faculty member at the Technical University of Lisbon. Vasco holds a PhD in Language Technologies from Carnegie Mellon University in the field of computational semantics. Additionally, Vasco is a Fulbright Scholar, mentor, and advisor to a number of startups on top of being a serial entrepreneur.

The "NariTra" multilingual translation app employs noise-canceling techniques and recognizes a wide range of speech. Offered by the airport at no cost, the app is designed to work hands-free -- and therefore suitable for foreign visitors who have just arrived and who have their hands full with luggage. The tests will see the app deployed on a shuttle bus running between Terminal 1 and Terminal 2, translating Japanese into English, Chinese and Korean, and vice versa. The airport operator plans to roll out the app by the time the 2020 Tokyo Olympic and Paralympic Games take place.

Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model–Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification.