The amazing thing about this new wave of innovation in AI is that the technology is democratized -- anyone can have near state-of-the-art technology to power their applications without paying for it.<p>One of the lasting principles in the "business of AI" is that the data is the true source of value. If you don't have a monopoly on the data, you're going to have a tough time sustaining a competitive business because everyone else will more or less be able to clone your technology. My PhD advisor taught me this based on his experience founding one of the first really successful neural networks companies, HNC, which was later acquired by Fair Isaac (FICO). HNC put together a consortium of banks in order to train their credit card fraud detection system and maintained a monopoly on the data. FICO still runs this business today and I've heard from recent employees it still runs on the same old single-hidden-layer multi-layer perceptron architecture as it did in the 90s.
I tested it on the live demo with a few sentences from Google News, English to Italian and viceversa. It looks to be at the same level of Google Translate: some sentences are better translated to Italian by OpenNMT, others by Google, even in the same excerpt from the news.<p>Both tools seems to be better at translating to English, but maybe because I don't realize some of the mistakes they do.<p>Overall it's a great tool. I suggest to read the FAQ at <a href="http://opennmt.net/FAQ/" rel="nofollow">http://opennmt.net/FAQ/</a>
Hi, I'm Alexander Rush (@harvardnlp), one of the project leads on OpenNMT and an assistant prof at Harvard. Feel free to ask me anything about the project.
Neural translation has been really amazing and impressive. I stumbled into a way to be mean to Google translate this week: <a href="https://twitter.com/driainmurray/status/818934530207862784" rel="nofollow">https://twitter.com/driainmurray/status/818934530207862784</a><p>OpenNMT can also run amok given totally unreasonable input too (example below). I'm being unfair, but the more serious point is that translation systems might go crazy in less obvious ways. While fluency is greatly improved, they certainly don't always get the meaning correct. I'm excited to see what people will do to better diagnose when we can trust the translation.<p>Input: <i>"Toll ach toll toll ach toll toll toll toll toll toll toll toll toll toll toll toll toll toll toll toll toll toll toll toll Deutsch."</i><p>Output: <i>"Tell me great great, great great, great great great great great great great great great love great ? ? ? great love ? ? ? ? ? ? ? great ? ? ? ? great love ? ? ? great great ? ? ? great great ? ? ? great great ? ? ? great great ? ? great great ? ? ? great great ? ? great great ? ? great love ? ? Good ? ? ? great love ? ? great love ? ? Good love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? great love ? ? Good ?"</i><p>[Each ? was a musical note glyph, but HN ate the unicode character.]
What resources does this require to run? Obviously you need some fairly heavy lifting for the initial training, but once trained, could the models be used on, say, a mobile phone?
These NMTs seem tragically bad at translating simple straightforward Chinese e.g.: 我要下班了。下班了再说。<p><pre><code> Google: I have to get off work. Off to say.
OpenNMT: I am going to work. After work, again.
</code></pre>
If this is the state of art for NLP. We have a long way to go.
Train top20 language pairs.<p>Make mobile app that takes picture, runs OCR, detects language and feeds into model.<p>Translate<p>???<p>Profit<p>All offline, after the models have loaded once.