Dial-MAE is a transformers based Masked Auto-Encoder post-training architecture designed for Retrieval-based Dialogue Systems. Details can be found in Dial-MAE:ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems(NAACL 2024)
Please refer to PyTorch Homepage to install a pytorch version suitable for your system.
Dependencies can be installed by running codes below. Specifically, we use transformers=4.17.0 for our experiments. Other versions should also work well.
apt-get install parallel
pip install transformers==4.17.0 datasets nltk tensorboard pandas tabulate
Please refer to examples below for reproducing our works.
If you find our work useful, please consider to cite our paper.
@inproceedings{su-etal-2024-dial,
title = "Dial-{MAE}: {C}on{T}extual Masked Auto-Encoder for Retrieval-based Dialogue Systems",
author = "Su, Zhenpeng and
W, Xing and
Zhou, Wei and
Ma, Guangyuan and
Hu, Songlin",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.47",
pages = "820--830",
abstract = "Dialogue response selection aims to select an appropriate response from several candidates based on a given user and system utterance history. Most existing works primarily focus on post-training and fine-tuning tailored for cross-encoders. However, there are no post-training methods tailored for dense encoders in dialogue response selection. We argue that when the current language model, based on dense dialogue systems (such as BERT), is employed as a dense encoder, it separately encodes dialogue context and response, leading to a struggle to achieve the alignment of both representations. Thus, we propose Dial-MAE (Dialogue Contextual Masking Auto-Encoder), a straightforward yet effective post-training technique tailored for dense encoders in dialogue response selection. Dial-MAE uses an asymmetric encoder-decoder architecture to compress the dialogue semantics into dense vectors, which achieves better alignment between the features of the dialogue context and response. Our experiments have demonstrated that Dial-MAE is highly effective, achieving state-of-the-art performance on two commonly evaluated benchmarks.",
}