Efficient Dynamic Hard Negative Sampling for Dialogue Selection

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",
}

Setup and Dependencies

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

Preparing Data and Checkpoints

Dataset and Checkpoints

Dataset

• Dataset for Knowledge Selection (DSTC9, DSTC10)
• Dataset for Response Selection (Ubuntu, E-commerce)

Original version for each dataset is availble in Ubuntu Corpus V1, E-Commerce Corpus, respectively.

Checkpoints

• Checkpoints (RoBERTa-large-EHDNS) for Knowledge Selection (DSTC9, DSTC10)
• Checkpoints (BERT-FP-EHDNS) for Response Selection (Ubuntu, E-commerce)

Training

Preprocess Data

For Knowledge Selection

DSTC9, DSTC10 dataset include processing python files.

For Response Selection

response_selection/ubuntu/preprocess_FT_ecom.py
response_selection/e-commerce/preprocess_FT_ecom.py

Traing and Test

Training (DSTC9, DSTC10, Ubuntu Corpus V1, E-commerce Corpus)

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.sh

Test (DSTC9, DSTC10, Ubuntu Corpus V1, E-commerce Corpus)

sh 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

Performance

For Knowledge Selection

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

For Response Selection

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