The task of Semantic Parsing can be approximated as a transformation of an utterance into a logical form graph where edges represent semantic roles and nodes represent word senses. The resulting representation should be capture the meaning of the utterance and be suitable for reasoning. Word senses and semantic roles are interdependent, meaning errors in assigning word senses can cause errors in assigning semantic roles and vice versa. While statistical approaches to word sense disambiguation outperform logical, rule-based semantic parsers for raw word sense assignment, these statistical word sense disambiguation systems do not produce the rich role structure or detailed semantic representation of the input. In this work, we provide hints from a statistical WSD system to guide a logical semantic parser to produce better semantic type assignments while maintaining the soundness of the resulting logical forms. We observe an improvement of up to 10.5% in F-score, however we find that this improvement comes at a cost to the structural integrity of the parse

中文翻译：

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用统计词义消歧提示语义解析

语义解析的任务可以近似为将话语转换为逻辑形式图，其中边代表语义角色，节点代表词义。由此产生的表示应该能够捕捉到话语的含义并适合推理。词义和语义角色是相互依赖的，这意味着分配词义的错误会导致分配语义角色的错误，反之亦然。虽然词义消歧的统计方法在原始词义分配方面优于基于逻辑的、基于规则的语义解析器，但这些统计词义消歧系统不会产生丰富的角色结构或输入的详细语义表示。在这项工作中，我们提供来自统计 WSD 系统的提示，以指导逻辑语义解析器生成更好的语义类型分配，同时保持结果逻辑形式的合理性。我们观察到 F-score 提高了 10.5%，但是我们发现这种改进是以解析结构完整性为代价的