3260 papers • 126 benchmarks • 313 datasets
( Image credit: SQL-to-Text Generation with Graph-to-Sequence Model )
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This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.
This work introduces a novel general end-to-end graph- to-sequence neural encoder-decoder model that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vectors.
This paper proposes a graph-to-sequence model to encode the global structure information into node embeddings that can effectively learn the correlation between the SQL query pattern and its interpretation.
Self-play improves the accuracy of a strong baseline on SParC and CoSQL, two widely used cross-domain text-to-SQL datasets, and enhances cross- domain generalization and improves beam-search.
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