Implements the model described in the following paper Efficient Dynamic Hard Negative Sampling for Dialogue Selection in ACL-NLP4ConvAI 2024.
@inproceedings{han-etal-2024-efficient,
title = "Efficient Dynamic Hard Negative Sampling for Dialogue Selection",
author = "Han, Janghoon and Lee, Dongkyu and Shin, Joongbo and Bae, Hyunkyung and Bang, Jeesoo and Kim, Seonghwan and Choi, Stanley Jungkyu and Lee, Honglak",
booktitle = "Proceedings of the 6th Workshop on NLP for Conversational AI (NLP4ConvAI 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nlp4convai-1.6",
pages = "89--100",
}
This code is implemented using PyTorch v1.10.0, and provides out of the box support with CUDA 11.3 Anaconda is the recommended to set up this codebase.
# https://pytorch.org
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r requirements.txt
Original version for each dataset is availble in Ubuntu Corpus V1, E-Commerce Corpus, respectively.
- Checkpoints (RoBERTa-large-EHDNS) for Knowledge Selection (DSTC9, DSTC10)
- Checkpoints (BERT-FP-EHDNS) for Response Selection (Ubuntu, E-commerce)
DSTC9, DSTC10 dataset include processing python files.
response_selection/ubuntu/preprocess_FT_ecom.py
response_selection/e-commerce/preprocess_FT_ecom.py
sh knowledge_selection/dstc9/train_dstc9_rlm_EDHNS.sh
sh knowledge_selection/dstc10/train_dstc10_rlm_EDHNS.sh
sh response_selection/ubuntu/train_bert_ubuntu.sh
sh response_selection/e-commerce/train_bert_ecom.shsh knowledge_selection/dstc9/test_dstc9_rlm_EDHNS.sh
sh knowledge_selection/dstc10/test_dstc10_rlm_EDHNS.sh
sh response_selection/ubuntu/test_bert_ubuntu.sh
sh response_selection/e-commerce/test_bert_ecom.sh| DSTC9 | R@1 | R@5 | MRR@5 |
|---|---|---|---|
| [RoBERTa-large-EDHNS] | 0.931 | 0.998 | 0.962 |
| DSTC10 | R@1 | R@2 | R@5 |
|---|---|---|---|
| [RoBERTa-large-EDHNS] | 0.821 | 0.935 | 0.869 |
| Ubuntu | R@1 | R@2 | R@5 |
|---|---|---|---|
| [BERT_FP-EDHNS] | 0.917 | 0.965 | 0.994 |
| E-Commerce | R@1 | R@2 | R@5 |
|---|---|---|---|
| [BERT_FP-EDHNS] | 0.957 | 0.986 | 0.997 |