Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: This blog post summarises the prototypical representation learning methods for knowledge extraction in low-resource (long-tail, few-shot, and zero-shot) settings.

Section 1 of the blog post nicely motivates the need for KE methods in low-resource settings. 

I have found Section 2 confusing and hard to follow. Firstly, it is not explained why prototypical representation learning methods are useful specifically for low-resource settings. Further, it is unclear to me how Figures 3 and 4 contribute towards understanding intra- and inter-prototype learning. More specifically, what exactly are Figure 3(b) and 4(b)&(c) trying to show? Figures 5 and 6 is missing. Section 2.3 is hard to parse and lacks details and examples as they appear in the paper (e.g. Figure 1 from the paper where it is shown that statements are sentences expressing relations between two entities).

The proposed research directions in Section 3 seem sound.

Overall, I have struggled more with following the blog post than the paper itself, which is why I believe that the blog post in its current form is not suitable for publication in the ICLR blog post track.
review_title: Review

Decision: Reject