Learning to Compose Task-Specific Tree Structures

@inproceedings{Choi2018LearningTC,
  title={Learning to Compose Task-Specific Tree Structures},
  author={Jihun Choi and Kang Min Yoo and Sang-goo Lee},
  booktitle={AAAI},
  year={2018}
}
For years, recursive neural networks (RvNNs) have been shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks. However, the main drawback of RvNNs is that they require structured input, which makes data preparation and model implementation hard. In this paper, we propose Gumbel Tree-LSTM, a novel tree-structured long short-term memory architecture that learns how to compose task-specific tree structures… CONTINUE READING
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