Papers by Yongzhen Wang
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases (2020.coling-main)
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| Challenge: | Existing methods for question generation over knowledge bases have low diversity and poor fluency due to the limited information contained in the subgraphs and semantic drift due to decoder’s oblivion of the semantics of the answer entity. |
| Approach: | They propose a knowledge-enriched, type-constrained and grammar-guided KBQG model that generates natural-language questions over a set of triples in the KB. |
| Outcome: | The proposed model outperforms existing methods on two widely-used benchmark datasets. |
Neural Related Work Summarization with a Joint Context-driven Attention Mechanism (D18-1)
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| Challenge: | Existing approaches to automatic related work summarization rely on human-engineered features. |
| Approach: | They propose a neural data-driven attention mechanism to measure contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. |
| Outcome: | The proposed approach achieves significant improvement over a typical seq2seq summarization baseline and five classical summarizing baselines. |