IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection

Overview of the proposed IRRGN

jupyter_version

jupyter_version is an unofficial version of the code, but you can use it to load model weights to verify the effect in the paper.

Code

You can train the model as follows:

   python code/main.py 

Checkpoints

Checkpoints on the base and plus datasets can be obtained through this link.

Environment

• A100-SXM4-80GB GPU

• CUDA 11.3

Citation

@inproceedings{DBLP:conf/emnlp/DengDGJP22,
  author       = {Jingcheng Deng and
                  Hengwei Dai and
                  Xuewei Guo and
                  Yuanchen Ju and
                  Wei Peng},
  editor       = {Yoav Goldberg and
                  Zornitsa Kozareva and
                  Yue Zhang},
  title        = {{IRRGN:} An Implicit Relational Reasoning Graph Network for Multi-turn
                  Response Selection},
  booktitle    = {Proceedings of the 2022 Conference on Empirical Methods in Natural
                  Language Processing, {EMNLP} 2022, Abu Dhabi, United Arab Emirates,
                  December 7-11, 2022},
  pages        = {8529--8541},
  publisher    = {Association for Computational Linguistics},
  year         = {2022},
  url          = {https://doi.org/10.18653/v1/2022.emnlp-main.584},
  doi          = {10.18653/v1/2022.emnlp-main.584},
  timestamp    = {Thu, 10 Aug 2023 12:35:22 +0200},
  biburl       = {https://dblp.org/rec/conf/emnlp/DengDGJP22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}