Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths

Resources and Codes for our ACL 2023 paper:

Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths. ACL 2023. (Outstanding Paper Award). [pdf]

Bibtex

@inproceedings{DBLP:conf/acl/ShenWX23,
  author       = {Xiangqing Shen and
                  Siwei Wu and
                  Rui Xia},
  editor       = {Anna Rogers and
                  Jordan L. Boyd{-}Graber and
                  Naoaki Okazaki},
  title        = {Dense-ATOMIC: Towards Densely-connected {ATOMIC} with High Knowledge
                  Coverage and Massive Multi-hop Paths},
  booktitle    = {Proceedings of the 61st Annual Meeting of the Association for Computational
                  Linguistics (Volume 1: Long Papers), {ACL} 2023, Toronto, Canada,
                  July 9-14, 2023},
  pages        = {13292--13305},
  publisher    = {Association for Computational Linguistics},
  year         = {2023},
  url          = {https://aclanthology.org/2023.acl-long.742},
  timestamp    = {Thu, 13 Jul 2023 16:47:40 +0200},
  biburl       = {https://dblp.org/rec/conf/acl/ShenWX23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Dense-ATOMIC

We currently release two versions of Dense-ATOMIC. More work is in progress.

Dense-ATOMIC-base:

baidu disk, google drive

total number	1153755
xNeed	157721
xIntent	201780
oAfter	224476
xAfter	322034
oPersona	91413
xPersona	156331

Dense-ATOMIC-large:

baidu disk, google drive

total number	10281235
xNeed	637624
xIntent	1104854
oAfter	1607797
xAfter	1964070
oPersona	2055146
xPersona	2911744

Rel-CSKGC

The Dada for training and testing Rel-CSKGC can be download: baidu_disk and google_drive.

Please unzip it under './Rel-CSKGC/' folder.

Environment

• Python 3.6.9
• Cuda 11.0
• Run pip install -r requirements.txt to install the required packages.

Training

We provide the Rek-CSKGC model here: baidu_disk and google_drive.

You can retrain the Rel-CSKGC model as following:

cd Rel-CSKGC
python run_training.py

Testing

You can evaluate the Rel-CSKGC model on our human annotated testing dataset as following:

python run_predicting.py

Creating Dense-ATOMIC

You can create the Dense-ATOMIC as following:

python run_completion.py