UNSUPERVISED LEARNING OF NEURAL SEMANTIC MAPPINGS WITH THE HUNGARIAN ALGORITHM FOR COMPOSITIONAL SEMANTICS | |
Zhang X(张翔)1; He SZ(何世柱)2; Liu K(刘康)2; Zhao J(赵军)2 | |
2024-04 | |
会议日期 | 2024-04 |
会议地点 | Seoul, South Korea |
英文摘要 | Neural semantic parsing maps natural languages (NL) to equivalent formal semantics which are compositional and deduce the sentence meanings by composing smaller parts. To learn a well-defined semantics, semantic parsers must recognize small parts, which are semantic mappings between NL and semantic tokens. Attentions in recent neural models are usually explained as one-on-one semantic mappings. However, attention weights with end-to-end training are shown only weakly correlated with human-labeled mappings. Despite the usefulness, supervised mappings are expensive. We propose the unsupervised Hungarian tweaks on attentions to better model mappings. Experiments have shown our methods is competitive with the supervised approach on performance and mappings recognition, and outperform other baselines. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/57637] ![]() |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | He SZ(何世柱) |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Zhang X,He SZ,Liu K,et al. UNSUPERVISED LEARNING OF NEURAL SEMANTIC MAPPINGS WITH THE HUNGARIAN ALGORITHM FOR COMPOSITIONAL SEMANTICS[C]. 见:. Seoul, South Korea. 2024-04. |
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