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Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Predicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained sequence-to-sequence Transformer models has recently led to large improvements on AMR parsing benchmarks. These parsers are simple and avoid explicit modeling of structure but lack desirable properties such as graph well-formedness guarantees or built-in graph-sentence alignments. In this work we explore the integration of general pre-trained sequence-to-sequence language models and a structure-awaredoi:10.18653/v1/2021.emnlp-main.507 fatcat:ewdruugcyzc2rcvl5xfoyxq7bu