yuningDING / Prompt-in-Argument-Identification

Experiment code of the paper "Don't Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing" accepted by BEA-NAACL-2022.

Prompt-in-Argument-Identification

Experiment code of the paper "Don't Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing" accepted by BEA-NAACL-2022.

How to use

1. Download data from: https://www.kaggle.com/c/feedback-prize-2021/ and save data to './data'

2. Install environment

   conda create --name env python=3.7
   conda activate env
   pip install -r experiment_requirements.txt

3. Split data into clusters and different experiment settings

   python ./data_split.py

4. Training model with different settings for 15 prompts. For example, the same_prompt setting will be trained by:

   for PROMPT in 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
   do
   python ./experiment_pipeline.py --train_prompt ${PROMPT} --validate_prompt ${PROMPT} --test_prompt ${PROMPT} --input ./data/same_prompt --model allenai/longformer-large-4096 --lr 1e-5 --output ./output --max_len 1536 --epochs 10
   done

How to cite

@inproceedings{ding-etal-2022-dont,
 title = "Don{'}t Drop the Topic - The Role of the Prompt in Argument Identification in Student Writing",
 author = "Ding, Yuning  and
   Bexte, Marie  and
   Horbach, Andrea",
 booktitle = "Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)",
 month = jul,
 year = "2022",
 address = "Seattle, Washington",
 publisher = "Association for Computational Linguistics",
 url = "https://aclanthology.org/2022.bea-1.17",
 doi = "10.18653/v1/2022.bea-1.17",
 pages = "124--133",
 }