of the paper include: - The introduction of relation embeddings for relation extraction is not new, for example look at all Knowledge graph completion approaches that explicitly model relation embeddings or works on distantly supervised relation extraction. However, an interesting experiment would be to show the impact that such embeddings can have by comparing with a simple baseline that does not take advantage of those.
- Improvements are incremental across datasets, with the exception of WebNLG. Why mean and standard deviation are not shown for the test set of DocRED?
- It is not clear if the benefit of the method is just performance-wise. Could this particular alignment of entity and relation embeddings (that gives the most in performance) offer some interpretability? ( perhaps this could be shown with a t-SNE plot, i.e. check that their embeddings are close in space).
Comments/Suggestions: - Lines 26-27: Multiple entities typically exist in both sentences and documents and this is the case even for relation classification, not only document-level RE or joint entity and relation extraction.
- Lines 39-42: Point to figure 1 for this particular example.
- Lines 97-98: Rephrase the sentence "one that searches for ... objects" as it is currently confusing - Line 181, Equations 4: $H^s$, $E^s$, $E^o$, etc are never explained.
- Could you show ablations on EPO and SEO? You mention in the Appendix that the proposed method is able to solve all those cases but you don't show if your method is better than others.
- It would be interesting to also show how the method performs when different number of triples reside in the input sequence. Would the method help more sequences with more triples?
Questions: - Improvement still be observed with a better encoder, e.g. RoBERTa-base, instead of BERT?
- How many seeds did you use to report mean and stdev on the development set?
- For DocRED, did you consider the documents as an entire sentence? How do you deal with concepts (multiple entity mentions referring to the same entity)? This information is currently missing from the manuscript.
- It would be interesting to also show how the method performs when different number of triples reside in the input sequence. Would the method help more sequences with more triples? Questions: