@inproceedings{shi-etal-2023-diffusion,
title = "A Diffusion Weighted Graph Framework for New Intent Discovery",
author = "Shi, Wenkai and
An, Wenbin and
Tian, Feng and
Zheng, Qinghua and
Wang, QianYing and
Chen, Ping",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.499",
doi = "10.18653/v1/2023.emnlp-main.499",
pages = "8033--8042",
abstract = "New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents. Without considering structure relationships between samples, previous methods generate noisy supervisory signals which cannot strike a balance between quantity and quality, hindering the formation of new intent clusters and effective transfer of the pre-training knowledge. To mitigate this limitation, we propose a novel $\textit{Diffusion Weighted Graph Framework}$ (DWGF) to capture both semantic similarities and structure relationships inherent in data, enabling more sufficient and reliable supervisory signals. Specifically, for each sample, we diffuse neighborhood relationships along semantic paths guided by the nearest neighbors for multiple hops to characterize its local structure discriminately. Then, we sample its positive keys and weigh them based on semantic similarities and local structures for contrastive learning. During inference, we further propose $\textit{Graph Smoothing Filter}$ (GSF) to explicitly utilize the structure relationships to filter high-frequency noise embodied in semantically ambiguous samples on the cluster boundary. Extensive experiments show that our method outperforms state-of-the-art models on all evaluation metrics across multiple benchmark datasets. Code and data will be made public.",
}
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<abstract>New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents. Without considering structure relationships between samples, previous methods generate noisy supervisory signals which cannot strike a balance between quantity and quality, hindering the formation of new intent clusters and effective transfer of the pre-training knowledge. To mitigate this limitation, we propose a novel Diffusion Weighted Graph Framework (DWGF) to capture both semantic similarities and structure relationships inherent in data, enabling more sufficient and reliable supervisory signals. Specifically, for each sample, we diffuse neighborhood relationships along semantic paths guided by the nearest neighbors for multiple hops to characterize its local structure discriminately. Then, we sample its positive keys and weigh them based on semantic similarities and local structures for contrastive learning. During inference, we further propose Graph Smoothing Filter (GSF) to explicitly utilize the structure relationships to filter high-frequency noise embodied in semantically ambiguous samples on the cluster boundary. Extensive experiments show that our method outperforms state-of-the-art models on all evaluation metrics across multiple benchmark datasets. Code and data will be made public.</abstract>
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%0 Conference Proceedings
%T A Diffusion Weighted Graph Framework for New Intent Discovery
%A Shi, Wenkai
%A An, Wenbin
%A Tian, Feng
%A Zheng, Qinghua
%A Wang, QianYing
%A Chen, Ping
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F shi-etal-2023-diffusion
%X New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents. Without considering structure relationships between samples, previous methods generate noisy supervisory signals which cannot strike a balance between quantity and quality, hindering the formation of new intent clusters and effective transfer of the pre-training knowledge. To mitigate this limitation, we propose a novel Diffusion Weighted Graph Framework (DWGF) to capture both semantic similarities and structure relationships inherent in data, enabling more sufficient and reliable supervisory signals. Specifically, for each sample, we diffuse neighborhood relationships along semantic paths guided by the nearest neighbors for multiple hops to characterize its local structure discriminately. Then, we sample its positive keys and weigh them based on semantic similarities and local structures for contrastive learning. During inference, we further propose Graph Smoothing Filter (GSF) to explicitly utilize the structure relationships to filter high-frequency noise embodied in semantically ambiguous samples on the cluster boundary. Extensive experiments show that our method outperforms state-of-the-art models on all evaluation metrics across multiple benchmark datasets. Code and data will be made public.
%R 10.18653/v1/2023.emnlp-main.499
%U https://aclanthology.org/2023.emnlp-main.499
%U https://doi.org/10.18653/v1/2023.emnlp-main.499
%P 8033-8042
Markdown (Informal)
[A Diffusion Weighted Graph Framework for New Intent Discovery](https://aclanthology.org/2023.emnlp-main.499) (Shi et al., EMNLP 2023)
ACL
- Wenkai Shi, Wenbin An, Feng Tian, Qinghua Zheng, QianYing Wang, and Ping Chen. 2023. A Diffusion Weighted Graph Framework for New Intent Discovery. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 8033–8042, Singapore. Association for Computational Linguistics.