Papers with HRG
Hierarchy Response Learning for Neural Conversation Generation (D19-1)
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| Challenge: | Neural conversation generation models can't perceive and express the intention effectively, causing dull and generic responses. |
| Approach: | They propose a hierarchical response generation model to capture conversation intention . they propose an expression reconstruction model and an expression attention model . |
| Outcome: | The proposed model can generate the responses with more appropriate content and expression. |
Exact yet Efficient Graph Parsing, Bi-directional Locality and the Constructivist Hypothesis (2020.acl-main)
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| Challenge: | Existing algorithms for graph parsing are exponential or high-degree polynomial w.r.t. grammars, and there are few systems that can parse large but frequent MRs with a realistic, wide-coverage grammar in a reasonable time. |
| Approach: | They propose an exact graph parsing algorithm that exploits locality as terminal edge-adjacency in HRG rules and categorizes a subclass of HRG. |
| Outcome: | The proposed method can parse graphs with a (competence) grammar in a time-efficient manner. |