Papers with DQG
Semantic Graphs for Generating Deep Questions (2020.acl-main)
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| Challenge: | Existing research has focused on generating factoid questions relevant to one fact obtainable from a single sentence. |
| Approach: | They propose a framework that first constructs a semantic-level graph and then encodes it by introducing an attention-based GGNN. |
| Outcome: | The proposed framework captures the global structure of the document and facilitates reasoning over multiple facts. |
Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)
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| Challenge: | Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information. |
| Approach: | They propose a deep question generation model that makes better use of the target answer as a guidance to facilitate question generation. |
| Outcome: | The proposed model outperforms state-of-the-art models in automatic and human evaluations on the hotpotQA dataset. |