Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification (2020.findings-emnlp)
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| Challenge: | Table-to-text models that select and order salient data and verbalize them fluently are lacking in content planning stage. |
| Approach: | They propose to enhance neural content planning by understanding data values with contextual numerical value representations that bring the sense of value comparison into content planning. |
| Outcome: | The proposed model outperforms existing systems with respect to content planning metrics on ROTOWIRE and MLB datasets. |
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Hayate Iso, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, Hiroya Takamura
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| Challenge: | Recent studies have shown improvement in generating descriptive text from structured data. |
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