code for Learning Structured Text Representations
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Learning Structured Text Representations

Code for the paper:

Learning Structured Text Representations
Yang Liu and Mirella Lapata, Accepted by TACL


This code is implemented with Tensorflow and the data preprocessing is with Gensim

Document Classification


The pre-processed YELP 2013 data can be downloaded at


To preprocess the data, run

python path-to-train path-to-dev path-to-test

This will generate a pickle file, the format for the input data can be found in the sample folder


python --data_file path_to_pkl --rnn_cell lstm --batch_size 16 --dim_str 50 --dim_sem 75 --dim_output 5 --keep_prob 0.7 --opt Adagrad
--lr 0.05 --norm 1e-4 --gpu -1 --sent_attention max --doc_attention max --log_period 5000

This will train the Tree-Matrix structured attention model in the paper on the training-set and present results on the devset/testset