Tree-Structured Long Short-Term Memory Networks

A PyTorch based implementation of Tree-LSTM from Kai Sheng Tai's paper Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.

Requirements

• PyTorch Deep learning library
• tqdm: display progress bar
• meowlogtool: a logger that write everything on console to file
• Java >= 8 (for Stanford CoreNLP utilities)
• Python >= 3

Usage

First run the script ./fetch_and_preprocess.sh

This downloads the following data:

• Stanford Sentiment Treebank (sentiment classification task)
• Glove word vectors (Common Crawl 840B) -- Warning: this is a 2GB download!

and the following libraries:

• Stanford Parser
• Stanford POS Tagger

Sentiment classification

python sentiment.py --name <name_of_log_file> --model_name <constituency|dependency> --epochs 10