DROID: Dual Representation for Out-of-Scope Intent Detection

• URL: http://arxiv.org/abs/2510.14110v1
• Date: Wed, 15 Oct 2025 21:29:52 GMT
• Title: DROID: Dual Representation for Out-of-Scope Intent Detection
• Authors: Wael Rashwan, Hossam M. Zawbaa, Sourav Dutta, Haytham Assem,
• Abstract summary: DROID is a compact framework that combines two complementary encoders -- the Universal Sentence Detection (USE) for broad semantic generalization and the Transformer-based Denoising Autoencoder (TSDAE) for domain-specific contextual distinctions.<n>Our results show that dual-encoder representations with simple calibration can yield robust, scalable, and reliable OOS detection for neural dialogue systems.
• Score: 4.768906732056304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting out-of-scope (OOS) user utterances remains a key challenge in task-oriented dialogue systems and, more broadly, in open-set intent recognition. Existing approaches often depend on strong distributional assumptions or auxiliary calibration modules. We present DROID (Dual Representation for Out-of-Scope Intent Detection), a compact end-to-end framework that combines two complementary encoders -- the Universal Sentence Encoder (USE) for broad semantic generalization and a domain-adapted Transformer-based Denoising Autoencoder (TSDAE) for domain-specific contextual distinctions. Their fused representations are processed by a lightweight branched classifier with a single calibrated threshold that separates in-domain and OOS intents without post-hoc scoring. To enhance boundary learning under limited supervision, DROID incorporates both synthetic and open-domain outlier augmentation. Despite using only 1.5M trainable parameters, DROID consistently outperforms recent state-of-the-art baselines across multiple intent benchmarks, achieving macro-F1 improvements of 6--15% for known and 8--20% for OOS intents, with the most significant gains in low-resource settings. These results demonstrate that dual-encoder representations with simple calibration can yield robust, scalable, and reliable OOS detection for neural dialogue systems.

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