One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets

@inproceedings{Damonte2021OneSP,
  title={One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets},
  author={Marco Damonte and Emilio Monti},
  booktitle={STARSEM},
  year={2021}
}
Semantic parsers map natural language utterances to meaning representations. The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets. To unify different datasets and train a single model for them, we investigate the use of Multi-Task Learning (MTL) architectures. We experiment with five datasets (Geoquery, NLMaps, TOP, Overnight, AMR). We find that an MTL architecture that shares the entire network across datasets yields… 

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