GHR

Capturing Conversational Interaction for Question Answering via Global History Reasoning

NAACL Findings 2022

We present GHR for conversational question answering (CQA). You can train ELECTRA by using our framework, GHR, described in our paper.

Requirements

$ conda create -n GHR python=3.8.10
$ conda activate GHR
$ conda install tqdm
$ conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.1 -c pytorch
$ pip install transformers==3.5.0

Datasets

We use the QuAC (Choi et al., 2018) dataset for training and evaluating our models, and test on the leaderboard.

Train

The following example fine-tunes ELECTRA on the QuAC dataset by using GHR. We performed all experiments using a single 16GB GPU (Tesla V100).

INPUT_DIR=./datasets/
OUTPUT_DIR=./tmp/model

CUDA_VISIBLE_DEVICES=0 python3 run_quac.py \
	--model_type electra  \
	--model_name_or_path   electra-large \
	--do_train \
	--do_eval \
        --data_dir ${INPUT_DIR} \
	--train_file train.json \
	--predict_file dev.json \
	--output_dir ${OUTPUT_DIR} \
	--per_gpu_train_batch_size 12 \
	--num_train_epochs 2 \
	--learning_rate 2e-5 \
	--weight_decay 0.01 \
	--threads 20 \
	--do_lower_case \
	--fp16 --fp16_opt_level "O2" \
	--evaluate_during_training \
	--max_answer_length 50 --cache_prefix electra-large

By default, we use mixed precision apex --fp16 for acceleration training and prediction.

Evaluation

The following example evaluates our trained model with the development set of QuAC.

INPUT_DIR=./datasets/
MODEL_DIR=./tmp/model/
OUTPUT_DIR=./tmp/

CUDA_VISIBLE_DEVICES=0 python3 run_quac.py \
	--model_type electra  \
	--model_name_or_path   ${MODEL_DIR} \
	--do_eval \
        --data_dir ${INPUT_DIR} \
	--train_file train.json \
	--predict_file dev.json \
	--output_dir ${OUTPUT_DIR} \
	--per_gpu_train_batch_size 12 \
	--num_train_epochs 2 \
	--learning_rate 2e-5 \
	--weight_decay 0.01 \
	--threads 20 \
	--do_lower_case \
	--fp16 --fp16_opt_level "O2" \
	--evaluate_during_training \
	--max_answer_length 50 --cache_prefix electra-large

Result

Evaluating models trained with predefined hyperparameters yields the following results:

DEV Results: {'F1': 74.9}  TEST Results: {'F1': 73.7}

Citation

@inproceedings{qian2022capturing,
  title={Capturing Conversational Interaction for Question Answering via Global History Reasoning},
  author={Qian, Jin and Zou, Bowei and Dong, Mengxing and Li, Xiao and Aw, Aiti and Hong, Yu},
  booktitle={Findings of the Association for Computational Linguistics: NAACL 2022},
  pages={2071--2078},
  year={2022}
}