Neural Tree Indexers for Text Understanding

Tsendsuren Munkhdalai, Hong Yu


Abstract
Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic treebased recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and node function. We implemented and evaluated a binary tree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification, outperforming state-of-the-art recurrent and recursive neural networks.
Anthology ID:
E17-1002
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–21
Language:
URL:
https://aclanthology.org/E17-1002
DOI:
Bibkey:
Cite (ACL):
Tsendsuren Munkhdalai and Hong Yu. 2017. Neural Tree Indexers for Text Understanding. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 11–21, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Neural Tree Indexers for Text Understanding (Munkhdalai & Yu, EACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/E17-1002.pdf
Code
 tsendeemts/nti
Data
SNLISST