This PyTorch package implements the KEAR model that surpasses human on the CommonsenseQA benchmark, as described in:

Yichong Xu, Chenguang Zhu, Shuohang Wang, Siqi Sun, Hao Cheng, Xiaodong Liu, Jianfeng Gao, Pengcheng He, Michael Zeng and Xuedong Huang
Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention
The 31st International Joint Conference on Artificial Intelligence (IJCAI), 2022.

The package also includes codes for our earilier DEKCOR model as in:

Yichong Xu∗, Chenguang Zhu∗, Ruochen Xu, Yang Liu, Michael Zeng and Xuedong Huang
Fusing Context Into Knowledge Graph for Commonsense Question Answering
Findings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021

Please cite the above papers if you use this code.

This package achieves the state-of-art performance of 86.1% (single model), 89.4% (ensemble) on the CommonsenseQA leaderboard, surpassing the human performance of 88.9%.

1. pull docker:
   > docker pull yichongx/csqa:human_parity

2. run docker
   > nvidia-docker run -it --mount src='/',target=/workspace/,type=bind yichongx/csqa:human_parity /bin/bash
> cd /workspace/path/to/repo
   Please refer to the following link if you first use docker: https://docs.docker.com/

Our code supports flexible training of various models on multiple choice QA.

• Distributed training with Pytorch native DDP or Deepspeed: see bash/task_train.sh
• Pause and resume training at any step; use option --continue_train
• Use any transformer encoders including ELECTRA, DeBERTa, ALBERT

Pre-processed data is located at data/.

We release codes for knowledge graph and dictionary external attention in preprocess/

1. Download data
   > cd preprocess
> bash download_data.sh
   
2. Add ConceptNet triples and Wiktionary definitions to data
   > python add_knowledge.py
   
3. We also add the most frequent relations in each question as a side information.
   > python add_freq_rel.py
   

1. train a model
   > bash bash/task_train.sh
2. make prediction
   > bash bash/task_predict.sh See task.py for available options.

The current code is mostly compatible to run DEKCOR. To run the original DEKCOR code, please checkout tag DEKCOR to use the previous version.

by Yichong Xu
[email protected]

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