NLP-progress VS tldr-transformers

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)

With the explosion in research on all things transformers, it seemed there was a need to have a single table to distill the "tl;dr" of each paper's contributions relative to each other. Here is what I got so far: https://github.com/will-thompson-k/tldr-transformers . Would love feedback - and feel free to contribute too :)

In any case, I'm liking the first glance so far. I'd just transpose the summary tables so they wouldn't get so tightly squeezed: https://github.com/will-thompson-k/tldr-transformers/blob/main/notes/bart.md

With the explosion in work on all things transformers, I felt the need to keep a single table of the "tl;dr" of various papers to distill their main takeaways: https://github.com/will-thompson-k/tldr-transformers . Would love feedback!