Feng Nie

Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments

Learning semantic correspondences between structured input data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation. Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, wh…