NeuralRST-BottomUp

About the code

The codebase is based on Koto et al. 2021, we translate their top-down architecture for bottom-up parsing. In turn, the encoder is designed based on Yu et al., 2018 where we use three main embeddings:

1. Word embedding, initialized by glove.6B.200d.txt.gz.
2. POS Tags embedding, initialized randomly.
3. Syntax Embedding from BiAffine Dependency Parser. Please refer to RSTExtractor to see how we extract it

Dependencies

1. Python 3.6
2. Run pip install -r requirements.txt

Data and Resource

We use English RST Tree Bank. Please make sure you have a right to access this data. Our code uses the input of the binarized discourse tree, provided by Yu et al., 2018.

In this repository, we do not provide you with the raw RST Tree Bank, but the binarized version split in train/dev/test based on Yu et al., 2018. We also provide the extracted syntax feature for each data split. Please download them here.

Running the code

In the experiment we use 1 GPU V100 (32GB).

For training the LSTM with static oracle (normal training)

CUDA_VISIBLE_DEVICES=0 python train_rst_parser.py bottom-up \
        --experiment=exp_static \
        --word_embedding_file=[path_to_glove] \
        --train=[path_to_train_data] --test=[path_to_test_data] --dev=[path_to_dev_data] \
        --train_syn_feat=[path_to_syntax_feature_of_train] \
        --test_syn_feat=[path_to_syntax_feature_of_test] \
        --dev_syn_feat=[path_to_syntax_feature_of_dev] \
        --subtree_order=random \
        --target_merges=all \
        --merge_inference=max \
        --word_embedding=glove \
        --max_sent_size=100 --hidden_size=256 --hidden_size_tagger=128 --batch_size=4 \
        --grad_accum=2 --lr=0.001 --ada_eps=1e-6 --gamma=1e-6 \
        --loss_seg=1.0 --loss_nuc_rel=1 \
        --depth_alpha=0 --elem_alpha=0 

For training the LSTM with the dynamic oracle:

CUDA_VISIBLE_DEVICES=0 python train_rst_parser.py bottom-up \
        --experiment=exp_dynamic \
        --word_embedding_file=[path_to_glove] \
        --train=[path_to_train_data] --test=[path_to_test_data] --dev=[path_to_dev_data] \
        --train_syn_feat=[path_to_syntax_feature_of_train] \
        --test_syn_feat=[path_to_syntax_feature_of_test] \
        --dev_syn_feat=[path_to_syntax_feature_of_dev] \
        --subtree_order=random \
        --target_merges=all \
        --merge_inference=max \
        --word_embedding=glove \
        --max_sent_size=100 --hidden_size=256 --hidden_size_tagger=128 --batch_size=4 \
        --grad_accum=2 --lr=0.001 --ada_eps=1e-6 --gamma=1e-6 \
        --loss_seg=1.0 --loss_nuc_rel=1 \
        --depth_alpha=0 --elem_alpha=0 \
        --use_dynamic_oracle=1 --start_dynamic_oracle=50 --oracle_prob=$1