Matches in ESWC 2020 for { ?s ?p The paper introduces a partial domain adaptive model for relation extraction. The authors argue that the method better accounts for negative transfer and therefore helps to improve F1 scores for the target domains. The paper is a bit hard to understand as the example "Baghdad, US" example is not very intuitive and throughout the paper it is unclear what type of relations this work actually supports: only "part of" (which I think is actually wrong in the case of Baghdad and US) or other types of relation such as "sibling", "parent", etc. This is important to gauge the potential impact of this work. A more comprehensive example that is used throughout the paper would help also this work. In the introduction there is a lot of terminology that is later-on not used very often (e.g., "GAN") or at all ("DS", "TL") while PDA (guess: "partial domain adaption") is not properly introduced. In general the paper is interesting so I would suggest to include some main improvements for a final version: write the introduction more focused on the actual task and have a nice running example for the whole work. Have the paper proof read due to some grammar/formulation issues.". }
Showing items 1 to 1 of
1
with 100 items per page.
- Paper.77_Review.0 hasContent "The paper introduces a partial domain adaptive model for relation extraction. The authors argue that the method better accounts for negative transfer and therefore helps to improve F1 scores for the target domains. The paper is a bit hard to understand as the example "Baghdad, US" example is not very intuitive and throughout the paper it is unclear what type of relations this work actually supports: only "part of" (which I think is actually wrong in the case of Baghdad and US) or other types of relation such as "sibling", "parent", etc. This is important to gauge the potential impact of this work. A more comprehensive example that is used throughout the paper would help also this work. In the introduction there is a lot of terminology that is later-on not used very often (e.g., "GAN") or at all ("DS", "TL") while PDA (guess: "partial domain adaption") is not properly introduced. In general the paper is interesting so I would suggest to include some main improvements for a final version: write the introduction more focused on the actual task and have a nice running example for the whole work. Have the paper proof read due to some grammar/formulation issues."".