Semantic dependency parser with reinforcement learning.
Tensorflow
We will publish off-the-shelve models soon.
- This requires the semantic dependency parsing datasets:
- The model also needs pretrained word mebedding vectors such as GloVe.
Firstly you need to prepare your datasets with embedding vectors as data.pkl.
python3 src/loaddata.py \
--tasks dm pas psd \
--splits train dev \
--datapkl data.pkl \
--vocab_min_freq 7 \
--emb path_to_glove_embedding_txt \
--file \
path_to_dm_train \
path_to_dm_dev \
path_to_pas_train \
path_to_pas_dev \
path_to_psd_train \
path_to_psd_dev \
Note that you don't need to parse full of the Glove vectors. Just prune uncommon words.
This is an example of training of DM formalism with supervised learning.
python src/semrl.py \
--train --parsing \
--tasks dm \
--inittasks dm pas psd \
--splits train dev \
--datapkl data.pkl \
--lstm_layers 3 \
--lstm_layers_dep 1 \
--fnn_hidden_dim 4000 \
--h_dim 600 --h_dep_dim 200 --emb_dep 100 \
--flag_dim 128 \
--droppout_rate_fnn 0.5 \
--epoch_max 40 \
--use_lemma \
--use_fnn3 \
--use_highway_fnn \
--use_highway_fnn2 \
--donotusenopred 3 \
--gold_easy_fast 0 \
--savepath path_to_save_model \
(To be updated soon for details and the labeling model...)
- Detailed Usage for training.
- Usage for parsing text files of CONLL format.
- Off-the-shelf parsing models for DM, PAS and PSD formalisms.