Describing a Knowledge Base

WS 2018 Qingyun WangXiaoman PanLifu HuangBoliang ZhangZhiying JiangHeng JiKevin Knight

We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new \emph{table position self-attention} to capture the inter-dependencies among related slots... (read more)

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Task Dataset Model Metric name Metric value Global rank Compare
KB-to-Language Generation Wikipedia Person and Animal Dataset KB-to-Language Generation Model BLEU 23.2 # 1
KB-to-Language Generation Wikipedia Person and Animal Dataset KB-to-Language Generation Model METEOR 23.4 # 1
KB-to-Language Generation Wikipedia Person and Animal Dataset KB-to-Language Generation Model ROUGE 42.0 # 1
Table-to-Text Generation Wikipedia Person and Animal Dataset KB-to-Language Generation Model BLEU 23.2 # 1
Table-to-Text Generation Wikipedia Person and Animal Dataset KB-to-Language Generation Model ROUGE 23.4 # 1
Table-to-Text Generation Wikipedia Person and Animal Dataset KB-to-Language Generation Model METEOR 42.0 # 1