lockon-n / kkt

Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge (in TASLP)

Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge

Part of codes and data for the paper Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge

KKT PART

Overview

Requirements

(Our experiment environment for reference)

Python 3.7

PyTorch (1.0.0)

NLTK (3.4.5)

Data

Raw data can be downloaded from the following urls:

​ DREAM: https://github.com/nlpdata/dream

​ ConceptNet: https://s3.amazonaws.com/conceptnet/downloads/2018/edges/conceptnet-assertions-5.6.0.csv.gz

We provides a processed version of DREAM and ConceptNet used in our experiment.

DREAM: in .KKT_code/dream_data/

​ We have written the relevance score and rank of each turn corresponding to QA in the json file.

​ We also have written the related knowledge IDs in ConceptNet for each example in the json file.

ConceptNet: in .KKT_code/conceptnet/

​ We have made a smaller version of ConceptNet, which only contains the English part.

Instructions

Here is a simple example of our experiment:

1. Get knowledge representation

python get_knowledge_representation.py 
--data_dir=../conceptnet/ 
--model_type=albert 
--model_name_or_path=albert-xxlarge-v2 
--task_name=knowledge 
--max_seq_length=40 
--do_lower_case 
--per_gpu_eval_batch_size=256

​ 2. Run the model

python run_multiple_choice.py 
--data_dir=../dream_data 
--model_type=albert_all 
--model_mode=kp 
--model_name_or_path=albert-xxlarge-v2 
--knowledge_file=../conceptnet/albert-xxlarge-v2_concept_knowledge_representations 
--task_name=dream 
--output_dir=../results/albert_xxlarge_dream 
--warmup_steps=0
--max_seq_length=512 
--top_k=6 
--do_train 
--do_eval 
--do_test 
--do_lower_case 
--per_gpu_train_batch_size=2 
--per_gpu_eval_batch_size=16 
--learning_rate=1e-5 
--num_train_epochs=2 
--save_steps=383 
--max_contexts_knowledge=30 
--eval_all_checkpoints 
--overwrite_output_dir 
--overwrite_cache

The results will be saved in ./results/albert_xxlarge_dream/my_eval_results.txt

Please kindly cite this paper if you use this repo.

@ARTICLE{9352490,  
  author={Zhang, Zhuosheng and Li, Junlong and Zhao, Hai},  
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},   
  title={Multi-Turn Dialogue Reading Comprehension With Pivot Turns and Knowledge},  
  year={2021},  
  volume={29},  
  number={},  
  pages={1161-1173},  
  doi={10.1109/TASLP.2021.3058616}
}