Generating Logical Forms from Graph Representations of Text and Entities

Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, Yasemin Altun


Abstract
Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing. Combined with a decoder copy mechanism, this approach provides a conceptually simple mechanism to generate logical forms with entities. We demonstrate that this approach is competitive with the state-of-the-art across several tasks without pre-training, and outperforms existing approaches when combined with BERT pre-training.
Anthology ID:
P19-1010
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
95–106
Language:
URL:
https://aclanthology.org/P19-1010
DOI:
10.18653/v1/P19-1010
Bibkey:
Cite (ACL):
Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, and Yasemin Altun. 2019. Generating Logical Forms from Graph Representations of Text and Entities. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 95–106, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Generating Logical Forms from Graph Representations of Text and Entities (Shaw et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1010.pdf
Video:
 https://vimeo.com/383957851