Abstract
Generative neural models have recently achieved state-of-the-art results for constituency parsing. However, without a feasible search procedure, their use has so far been limited to reranking the output of external parsers in which decoding is more tractable. We describe an alternative to the conventional action-level beam search used for discriminative neural models that enables us to decode directly in these generative models. We then show that by improving our basic candidate selection strategy and using a coarse pruning function, we can improve accuracy while exploring significantly less of the search space. Applied to the model of Choe and Charniak (2016), our inference procedure obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior state-of-the-art results for single-model systems.- Anthology ID:
- D17-1178
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1695–1700
- Language:
- URL:
- https://aclanthology.org/D17-1178
- DOI:
- 10.18653/v1/D17-1178
- Cite (ACL):
- Mitchell Stern, Daniel Fried, and Dan Klein. 2017. Effective Inference for Generative Neural Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1695–1700, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Effective Inference for Generative Neural Parsing (Stern et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D17-1178.pdf
- Data
- Penn Treebank