Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence (2020.acl-main)
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| Challenge: | Recent neural attention models conflate all steps into a single end-to-end system and simplify training process. |
| Approach: | They propose to explicitly segment target text into fragment units and align them with their data correspondences. |
| Outcome: | The proposed model outperforms neural attention models on E2E and WebNLG benchmarks. |
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