We did not add Fasttext to our benchmarks because of a minor technical issue but we will work on it. Further, to conduct a fair comparison with ELMo, I think it is needed to use extrinsic tasks such as question answering and textual entailment.
You can build very simple 100 lines benchmark e.g. doing IMDB dataset classification, and use ELMO and your embeddings, and see who will perform better..
Thank you for your feedback! I am also interested in conducting experiments on extrinsic tasks such as text classification. In addition to word embeddings, Wikipedia2Vec also contains entity embeddings which are likely beneficial for these tasks, so I would like to design a model that uses both the word embeddings and entity embeddings.