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Tree-LSTM

This is a re-implementation of the following paper:

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks Kai Sheng Tai, Richard Socher, and Christopher Manning.

The provided implementation can achieve a test accuracy of 51.72 which is comparable with the result reported in the original paper: 51.0(±0.5).

Dependencies

  • MXNet nightly build
  • requests
  • nltk
pip install mxnet --pre
pip install requests nltk

Data

The script will download the [SST dataset] (http://nlp.stanford.edu/sentiment/index.html) and the GloVe 840B.300d embedding automatically if --use-glove is specified (note: download may take a while).

Usage

DGLBACKEND=mxnet python3 train.py --gpu 0

Speed Test

See https://docs.google.com/spreadsheets/d/1eCQrVn7g0uWriz63EbEDdes2ksMdKdlbWMyT8PSU4rc .

Note

The code can work with MXNet 1.5.1