Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments (2020.coling-main)
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| Challenge: | Existing methods for data-to-text generation use a large-scale training corpus to learn semantic correspondences between structured input data and associated texts. |
| Approach: | They propose a local-to-global alignment framework that uses local and global models to learn semantic correspondences from large-scale datasets. |
| Outcome: | The proposed framework can be generalized to restaurant and computer domains and improve alignment accuracy. |
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