SpanishDict VS Apertium

I was always looking words up on either SpanishDict or using Google Translate, DeepL and the apps' builtin translation tools. - Source: dev.to / 10 months ago

I'm currently trying to learn some basic Portuguese and I'm having a hard time finding a nice consistent way to build my vocabulary. I already know quite a bit of Spanish (I'd consider myself a B1-B2ish speaker) and I have tutors I talk to every week of both languages. I mostly use SpanishDict.com for my vocabulary because I find it easy to find lists and examples, it's okay for testing pronunciation, and it's a... Source: about 1 year ago

First it started with only spanishdict.com and then pretty much any HTTPS site. Source: about 1 year ago

You made a few. Also for translation services DeepL is better than google translate, and you should know the difference between using a dictionary and using a translation service. In this case, a dictionary is what you want since you're trying to find the meaning of a word and its gender. If you had used spanishdict.com it would've explained to you what was happening. Source: about 1 year ago

For me, the method depends mainly from the resources I have. For Spanish I'm using spanishdict.com and it has a convienient way to create lists of words, so I just save a new word I see and then after some time I convert that into flashcards. For German I'm using collinsdictionary.com which I use as a normal dictionary, so usually I either create new cards while immersing myself or write them down to a file and... Source: about 1 year ago

This is very cool, looking forward to it! I've been doing the same thing with Spanish Wikipedia articles for a while, using a few lines of Bash + Regex. I was using Apertium for it. https://apertium.org/ It's definitely worse than most ML-based solutions, but it works reliably and fast; you can run it entirely offline. With Spanish translations, the main problem I was facing is lack of vocabulary, so I created - Source: Hacker News / 9 months ago

I used to keep track of the state of machine translation some years back. I think the way you measure the success of an automated translation is edit distance, i.e. How many manual edits you need to make to a translated text before you reach some acceptable state. I suppose it's somewhat subjective, but it is possible to construct a benchmark and allow for multiple correct results. The best resources I knew back... - Source: Hacker News / almost 2 years ago

Apertium is one of them. We make open-source rule-based machine translation systems, and our core tools are in C++. A few of our proposed ideas involve modifying those C++ tools with new features or improvements to existing features. Source: about 3 years ago