Code for the ACL 2019 paper:
Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader
Paper link: https://arxiv.org/abs/1905.07098
Model Overview:
Requirements
PyTorch 1.0.1tensorboardXtqdmgluonnlp
Prepare data
mkdir datasets && cd datasets && wget https://sites.cs.ucsb.edu/~xwhan/datasets/webqsp.tar.gz && tar -xzvf webqsp.tar.gz && cd ..
Full KB setting
CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_full_kb --max_num_neighbors 50 --label_smooth 0.1 --data_folder datasets/webqsp/full/
Incomplete KB setting
Note: The Hits@1 should match or be slightly better than the number reported in the paper. More tuning on threshold should give you better F1 score.
30% KB
CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_kb_03 --max_num_neighbors 50 --use_doc --data_folder datasets/webqsp/kb_03/ --eps 0.05
10% KB
CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_kb_01 --max_num_neighbors 50 --use_doc --data_folder datasets/webqsp/kb_01/ --eps 0.05
50% KB
CUDA_VISIBLE_DEVICES=0 python train.py --model_id KAReader_kb_05 --num_layer 1 --max_num_neighbors 100 --use_doc --data_folder datasets/webqsp/kb_05/ --eps 0.05 --seed 3 --hidden_drop 0.05
Citation
@inproceedings{xiong-etal-2019-improving,
title = "Improving Question Answering over Incomplete {KB}s with Knowledge-Aware Reader",
author = "Xiong, Wenhan and
Yu, Mo and
Chang, Shiyu and
Guo, Xiaoxiao and
Wang, William Yang",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1417",
doi = "10.18653/v1/P19-1417",
pages = "4258--4264",
}