Papers › LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network

LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network

6 Oct 2021arXiv:2110.15717archive 2025-07-28

Vibhav Agarwal, Sudeep Deepak Shivnikar, Sourav Ghosh, Himanshu Arora, Yashwant Saini

Intent detection is a crucial task in any Natural Language Understanding (NLU) system and forms the foundation of a task-oriented dialogue system. To build high-quality real-world conversational solutions for edge devices, there is a need for deploying intent detection model on device. This necessitates a light-weight, fast, and accurate model that can perform efficiently in a resource-constrained environment. To this end, we propose LIDSNet, a novel lightweight on-device intent detection model, which accurately predicts the message intent by utilizing a Deep Siamese Network for learning better sentence representations. We use character-level features to enrich the sentence-level representations and empirically demonstrate the advantage of transfer learning by utilizing pre-trained embeddings. Furthermore, to investigate the efficacy of the modules in our architecture, we conduct an ablation study and arrive at our optimal model. Experimental results prove that LIDSNet achieves state-of-the-art competitive accuracy of 98.00% and 95.97% on SNIPS and ATIS public datasets respectively, with under 0.59M parameters. We further benchmark LIDSNet against fine-tuned BERTs and show that our model is at least 41x lighter and 30x faster during inference than MobileBERT on Samsung Galaxy S20 device, justifying its efficiency on resource-constrained edge devices.

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Tasks

Intent Detection Natural Language Understanding Sentence Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Intent Detection	ATIS	LIDSNet	Accuracy	95.97	#14 of 16	Archive leaderboard	report
Intent Detection	SNIPS	LIDSNet	Accuracy	98.0	#6 of 10	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Attention BiLSTM Dense Connections LSTM Layer Normalization Linear Layer MobileBERT Multi-Head Attention Residual Connection Siamese Network Sigmoid Activation Softmax Tanh Activation

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