Matches in ESWC 2020 for { ?s ?p ?o. }
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- Author.77.2 label "Juan Yang, 2nd Author for Paper 77".
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- Author.77.3 label "Xiangbin Meng, 3rd Author for Paper 77".
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- Paper.77_Review.0 issued "2001-01-16T09:40:00.000Z".
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- 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."".
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- Paper.77_Review.1 hasContent "This paper presents an approach that leverages domain adaptation and adversarial learning to improve relation extraction. The paper is well written and detailed. Sometimes I miss some concrete examples to illustrate the different steps, and it is not always is easy to follow given the mixture of techniques applied and the number of acronyms. Some minor comments: - In the intro it is not clear the Model collapse explanation, I would need more context or a more detailed example. - When Fig. 1 appears there is no previous description about PDA, and even PDA is not referenced in the text at any moment, only in the image caption. I missing some explanation here. - In Section 2.2 it says that many studies have proved that domain adaptive models based on DL are better than DL models with sparse data. I would need more context here, studies only on relation extraction or this is a more general assumption? - Fig. 2 is a bit hard to understand - Section 4.1, I'm missing a more detailed explanation about the dataset, what kind of dataset, you are assuming that the reader knows the dataset. All in all I recommend a rewriting trying to make the paper easier to understand for readers not experts in relation extraction."".
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- Paper.78 label "Best Bang for the Buck: Purchase Allocation Under Access Dependent Pricing Functions".
- Paper.78 title "Best Bang for the Buck: Purchase Allocation Under Access Dependent Pricing Functions".
- Paper.78 issued "2001-12-03T14:48:00.000Z".
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