OPUS-MT-train VS NLP-progress

https://translatelocally.com/ is a nice gui around marian/bergamot. So far not very many bundled pairs, though I would guess any of the models from https://github.com/Helsinki-NLP/Opus-MT-train/tree/master/mo... and https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/maste... should be usable.

There is also Apertium, a rule-based system which is very good for some closely-related pairs that have had a lot of work put into them (especially translation between Romance languages, e.g. Spanish→Catalan, and Norwegian Bokmål→Nynorsk), and the only OK translator for some lesser-resourced languages (e.g. Northern Saami→Norwegian Bokmål), but very underdeveloped for anything to/from English (it feels a bit pointless writing rules for English where there is so much available data; RBMT shines where there's not enough available data, ie. most of the languages of the world)

One thing is that NLP is a set of wildly different problems which share some aspects, but often use quite different techniques and assumptions about their datasets. So even if you would have NLP experience, if you'd need to start on a substantially different NLP task, you can't just apply what you know and succeed, you have to review "how things are done" for that problem domain. For a quick overview, sites like https://nlpprogress.com/ can be helpful to see what methods are used; and, perhaps even more importantly, how people are modeling the actual task.

A standard step in Chinese text processing is word segmentation, which deals with this problem.

NLP has a github project tracking NLP progress, https://github.com/sebastianruder/NLP-progress. I wanna know if there is one tracking computer vision progress.

It would also be cool to have some charts with parameter density and even overall effectiveness (a tl;dr version of SOTA-trackers, maybe?) if that doesn't prove too infeasible.

As I said It increase ambiguity and cognitive overheard, needlessly given that "it" exists. Moreover it also make it harder for artificial intelligence to understand human text https://github.com/sebastianruder/NLP-progress/blob/master/english/coreference_resolution.md

I could not find it on the internet (including on GitHub, Kaggle, Medium, or Reddit.) And, I know about NLP Progress and The Super Duper NLP Repo.

For someone who needs to be on top of the latest research - Twitter (distraction-prone, marketing-friendly, instantly-gratifying, quick), newsletters in ML + NLP (https://jack-clark.net/, ruder.io, offconvex.org, etc.) (distraction-free, generic, time-consuming), SOTA chasing (https://paperswithcode.com/, http://nlpprogress.com/) (distraction-free, generic + focused, code-friendly)