Dialogue Graph Modeling for Conversational Machine Reading

This is the code for the paper Dialogue Graph Modeling for Conversational Machine Reading.

Here is a codalab bundle link to reproduce our results.

1. Requirements

(Our experiment environment for reference)

• Python 3.6

• Python 2.7 (for open discourse tagging tool)

• Pytorch (1.6.0)

• NLTK (3.4.5)

• spacy (2.0.16)

• transformers (2.8.0)

• editdistance (0.5.2)

• dgl (0.5.3)

2. Datasets

Download the dataset and extract it, or to use it directly in the directory data/sharc_raw

3. Instructions

3.1 Preprocess Data

Fixing errors in raw data

python fix_question.py

EDU segmentation

The environment requirements are listed here

cd segedu
python preprocess_discourse_segment.py
python sharc_discourse_segmentation.py

Discourse relations tagging

We need to train a discourse relation tagging model according to here. Firstly, download Glove for pretrained word vector and put it in DialogueDiscourseParsing/glove/glove.6B.100d.txt.

Secondly, preprocess data for training.

python data_pre.py <input_dir> <output_file>

Or you can directly use the data in DialogueDiscourseParsing/data/processed_data.

Then train the parser with

python main.py --train

The model should be stored in DialogueDiscourseParsing/dev_model. One can directly use the model trained here.

Finally, we can inference for ShARC dataset to get the discourse relations.

python construct_tree_mapping.py
python convert.py

cd DialogueDiscourseParsing
python main_.py

Preprocessing for Decision Making

python preprocess_decision_base.py

Preprocessing for Question Generation

python preprocess_span.py

All the preprocessed data can be found in the directory ./data. You can also download it here

3.2 Decision Making and Question Generation

To train the model on decision making subtask, run the following:

python -u train_sharc.py \
--train_batch=16 \
--gradient_accumulation_steps=2 \
--epoch=5 \
--seed=323 \
--learning_rate=5e-5 \
--loss_entail_weight=3.0 \
--dsave="out/{}" \
--model=decision_gcn \
--early_stop=dev_0a_combined \
--data=./data/ \
--data_type=decision_electra-large-discriminator \
--prefix=train_decision \
--trans_layer=2 \
--eval_every_steps=300

The trained model and corresponding results are stored in out/train_decision

For question generation subtask, we first extract the under-specified span by following:

python -u train_sharc.py \
--train_batch=16 \
--gradient_accumulation_steps=2 \
--epoch=5 \
--seed=115 \
--learning_rate=5e-5 \
--dsave="out/{}" \
--model=span \
--early_stop=dev_0_combined \
--data=./data/ \
--data_type=span_electra-large-discriminator \
--prefix=train_span \
--eval_every_steps=100

The trained model and corresponding results are stored in out/train_span

Then, use the inference result of under-specified span and the rule document to generate follow-up questions:

python -u qg.py \
--fin=./data/sharc_raw/json/sharc_dev.json \
--fpred=./out/inference_span \  # directory of span prediction
--model_recover_path=/absolute/path/to/pretrained_models/qg.bin \
--cache_path=/absolute/path/to/pretrain_models/unilm/

The final results are stored in final_res.json

Acknowledgement

Part of code is modified from the [Discern](https://github.com/Yifan-Gao/Discern) implementation.