Semantic Representation for Dialogue Modeling
- Tencent AI Lab（腾讯人工智能实验室）
- Zhejiang University（浙江大学）
- Westlake University（西湖大学）
- Westlake Institute for Advanced Study（西湖高等研究院）
机构由 AI 辅助整理，请以论文原文为准。
英文摘要：
Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity. We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems. Experimental results on both dialogue understanding and response generation tasks show the superiority of our model. To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling.
