Abstract
Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models have not leveraged continuous word representations. In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. We show that the invertibility condition allows for efficient exact inference and marginal likelihood computation in our model so long as the prior is well-behaved. In experiments we instantiate our approach with both Markov and tree-structured priors, evaluating on two tasks: part-of-speech (POS) induction, and unsupervised dependency parsing without gold POS annotation. On the Penn Treebank, our Markov-structured model surpasses state-of-the-art results on POS induction. Similarly, we find that our tree-structured model achieves state-of-the-art performance on unsupervised dependency parsing for the difficult training condition where neither gold POS annotation nor punctuation-based constraints are available.- Anthology ID:
- D18-1160
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1292–1302
- Language:
- URL:
- https://aclanthology.org/D18-1160
- DOI:
- 10.18653/v1/D18-1160
- Cite (ACL):
- Junxian He, Graham Neubig, and Taylor Berg-Kirkpatrick. 2018. Unsupervised Learning of Syntactic Structure with Invertible Neural Projections. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1292–1302, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Learning of Syntactic Structure with Invertible Neural Projections (He et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D18-1160.pdf
- Code
- jxhe/struct-learning-with-flow
- Data
- PTB Diagnostic ECG Database, Penn Treebank