Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations

Taeuk Kim, Jihun Choi, Daniel Edmiston, Sanghwan Bae, Sang-goo Lee
9/7/2018
cs.CL

Official code: galsang/SATA-Tree-LSTM (10 stars, last commit 2022-06-21).

Abstract

Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing. We present a novel RvNN architecture that can provide dynamic compositionality by considering comprehensive syntactic information derived from both the structure and linguistic tags. Specifically, we introduce a structure-aware tag representation constructed by a separate tag-level tree-LSTM. With this, we can control the composition function of the existing word-level tree-LSTM by augmenting the representation as a supplementary input to the gate functions of the tree-LSTM. In extensive experiments, we show that models built upon the proposed architecture obtain superior or competitive performance on several sentence-level tasks such as sentiment analysis and natural language inference when compared against previous tree-structured models and other sophisticated neural models.

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Code Implementations(1)

galsang/SATA-Tree-LSTMOfficial100%

Implementation of SATA Tree-LSTM (Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations, AAAI 2019)

104PythonNov 1, 20184 years ago

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Cite this paper

@article{kim2018dynamic,
  title  = {Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations},
  author = {Taeuk Kim and Jihun Choi and Daniel Edmiston and Sanghwan Bae and Sang-goo Lee},
  year   = {2018},
  eprint = {1809.02286},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1809.02286}
}

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