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Papers/Describing a Knowledge Base

Describing a Knowledge Base

Qingyun Wang, Xiaoman Pan, Lifu Huang, Boliang Zhang, Zhiying Jiang, Heng Ji, Kevin Knight

2018-09-06WS 2018 11Data-to-Text GenerationText GenerationTable-to-Text GenerationKB-to-Language Generation
PaperPDFCode(official)

Abstract

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. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8% - 72.6% F-score.

Results

TaskDatasetMetricValueModel
Text GenerationWikipedia Person and Animal DatasetBLEU23.2KB-to-Language Generation Model
Text GenerationWikipedia Person and Animal DatasetMETEOR42KB-to-Language Generation Model
Text GenerationWikipedia Person and Animal DatasetROUGE23.4KB-to-Language Generation Model
Text GenerationWikipedia Person and Animal DatasetBLEU23.2KB-to-Language Generation Model
Text GenerationWikipedia Person and Animal DatasetMETEOR23.4KB-to-Language Generation Model
Text GenerationWikipedia Person and Animal DatasetROUGE42KB-to-Language Generation Model
Table-to-Text GenerationWikipedia Person and Animal DatasetBLEU23.2KB-to-Language Generation Model
Table-to-Text GenerationWikipedia Person and Animal DatasetMETEOR42KB-to-Language Generation Model
Table-to-Text GenerationWikipedia Person and Animal DatasetROUGE23.4KB-to-Language Generation Model
Table-to-Text GenerationWikipedia Person and Animal DatasetBLEU23.2KB-to-Language Generation Model
Table-to-Text GenerationWikipedia Person and Animal DatasetMETEOR23.4KB-to-Language Generation Model
Table-to-Text GenerationWikipedia Person and Animal DatasetROUGE42KB-to-Language Generation Model

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