I know this is a blog post and not a research paper, but I see two big flaws in it that may mislead the reader: (i) failing to acknowledge that this same task

I know this is a blog post and not a research paper, but I see two big flaws in it that may mislead the reader: (i) failing to acknowledge that this same task (semantic role labelling / semantic graph generation) is performed by many existing NLP tools and not even hinting at some comparison results. One would think Semantic Graphs (c) are a novel thing invented at Google (TM). (ii) Presenting the fact that all the intermediate features are magically computed inside a black box as an improvement. This is, to me, a huge loss. There is great value in this classical NLP pipeline they so much criticise, especially in terms of explainability: "Why is mail an agent in the semantic graph? Because the tagger wrongly tagged it as a noun". I can (easily enough) correct the above in a classical pipeline, while I'm wondering how should I stir those neurons in Google's implementation to fix it. Anyway, this ended up a bit more ranty than I expected. I actually am looking forward to try this out and see how well it works. I think it's very positive that google open sources their research projects, as I find this is surprisingly uncommon in academia.