Abstract
While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence.- Anthology ID:
- P17-2059
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 372–377
- Language:
- URL:
- https://aclanthology.org/P17-2059
- DOI:
- 10.18653/v1/P17-2059
- Cite (ACL):
- Hongyu Guo. 2017. A Deep Network with Visual Text Composition Behavior. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 372–377, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Deep Network with Visual Text Composition Behavior (Guo, ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P17-2059.pdf