Recursive Top-Down Production for Sentence Generation with Latent Trees
Shawn Tan, Yikang Shen, Alessandro Sordoni, Aaron Courville, Timothy J. O’Donnell
Abstract
We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic programming algorithm that marginalises over latent binary tree structures with N leaves, allowing us to compute the likelihood of a sequence of N tokens under a latent tree model, which we maximise to train a recursive neural function. We demonstrate performance on two synthetic tasks: SCAN, where it outperforms previous models on the LENGTH split, and English question formation, where it performs comparably to decoders with the ground-truth tree structure. We also present experimental results on German-English translation on the Multi30k dataset, and qualitatively analyse the induced tree structures our model learns for the SCAN tasks and the German-English translation task.- Anthology ID:
- 2020.findings-emnlp.208
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2291–2307
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.208
- DOI:
- 10.18653/v1/2020.findings-emnlp.208
- Cite (ACL):
- Shawn Tan, Yikang Shen, Alessandro Sordoni, Aaron Courville, and Timothy J. O’Donnell. 2020. Recursive Top-Down Production for Sentence Generation with Latent Trees. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2291–2307, Online. Association for Computational Linguistics.
- Cite (Informal):
- Recursive Top-Down Production for Sentence Generation with Latent Trees (Tan et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.findings-emnlp.208.pdf
- Data
- SCAN