Papers with attribute embedding

2 papers
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (D18-1)

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Challenge: Existing approaches to align multilingual knowledge graphs with counterparts in different languages are not effective.
Approach: They propose a novel approach for cross-lingual KG alignment via graph convolutional networks . they train GCNs to embed entities of each language into a unified vector space .
Outcome: The proposed approach gets the best performance on real multilingual KGs compared with other embedding-based approaches.
Attribute-aware Sequence Network for Review Summarization (D19-1)

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Challenge: Existing review summarization systems generate summary only based on review content and neglect the authors’ attributes (e.g., gender, age, and occupation).
Approach: They propose an Attribute-aware Sequence Network (ASN) to take the aforementioned users’ characteristics into account by encoding their attributes over the words.
Outcome: The proposed model outperforms existing systems on tripAtt and human evaluation by taking the authors' attributes into account and incorporating attribute embedding and word-using habits into word prediction.

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