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- Paper.77_Review.1 type ReviewVersion.
- Paper.77_Review.1 issued "2001-01-18T07:56:00.000Z".
- Paper.77_Review.1 creator Paper.77_Review.1_Reviewer.
- Paper.77_Review.1 hasRating ReviewRating.1.
- Paper.77_Review.1 hasReviewerConfidence ReviewerConfidence.3.
- Paper.77_Review.1 reviews Paper.77.
- Paper.77_Review.1 issuedAt easychair.org.
- Paper.77_Review.1 issuedFor Conference.
- Paper.77_Review.1 releasedBy Conference.
- 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."".