GitHub - ruixiangcui/implicit_parser

Implicit UCCA Parser

This repository accompanies the paper, "Great Service! Fine-grained Parsing of Implicit Arguments", providing codes to train models and pre/post-precessing mrp dataset.

master branch contains Implicit-eager parser, alternative branch contains Implicit-standard parser

Pre-requisites

• Python 3.6
• NLTK
• Gensim
• Penman
• AllenNLP 0.9.0

Dataset

Total training data is available at [mrp-data].

Model

For prediction, please specify the BERT path in config.json to import the bert-indexer and bert-embedder. More prediction commands could be found in bash/predict.sh.

About BERT version, we use wwm_cased_L-24_H-1024_A-16.

Usage

Prepare data

We use conllu format companion data. This command adds companion.conllu to data.mrp and outputs to data.aug.mrp

bash bash/get_imp.sh

### Train the parser

Based on AllenNLP, the training command is like

```shell script
CUDA_VISIBLE_DEVICES=${gpu_id} \
TRAIN_PATH=${train_set} \
DEV_PATH=${dev_set} \
BERT_PATH=${bert_path} \
WORD_DIM=${bert_output_dim} \
LOWER_CASE=${whether_bert_is_uncased} \
BATCH_SIZE=${batch_size} \
    allennlp train \
        -s ${model_save_path} \
        --include-package utils \
        --include-package modules \
        --file-friendly-logging \
        ${config_file}

Refer to bash/train_imp.sh for more and detailed examples.

Predict with the parser

The predicting command is like

CUDA_VISIBLE_DEVICES=${gpu_id} \
    allennlp predict \
        --cuda-device 0 \
        --output-file ${output_path} \
        --predictor ${predictor_class} \
        --include-package utils \
        --include-package modules \
        --batch-size ${batch_size} \
        --silent \
        ${model_save_path} \
        ${test_set}

Refer to bash/predict_imp.sh for more and detailed examples.

Package structure

• bash/ command pipelines and examples
• config/ Jsonnet config files
• metrics/ metrics used in training and evaluation
• modules/ implementations of modules
• toolkit/ external libraries and dataset tools
• utils/ code for input/output and pre/post-processing

Contacts

For further information, please contact rc@di.ku.dk