用双编码器融合通用与领域语义，实现无需后处理的精准意图外检测。
DROID: Dual Representation for Out-of-Scope Intent Detection
- 双编码器：通用语义+领域自适应上下文，互补增强表示
- 仅150万参数，对已知/未知意图的宏F1提升6-20%
- 适合低资源场景，无需复杂校准模块，部署轻量
在任务导向对话系统中，识别超出范围（OOS）用户输入仍是关键挑战。现有方法常依赖强分布假设或额外校准模块。本文提出DROID（双表示用于超范围意图检测），一种紧凑的端到端框架，结合两种互补编码器：用于广义语义泛化的通用句子编码器（USE）和用于领域特定上下文区分的领域适配型Transformer去噪自编码器（TSDAE）。其融合表示由轻量级分支分类器处理，仅通过单一校准阈值即可分离域内与OOS意图，无需后处理评分。为增强有限监督下的边界学习，DROID引入合成及开放域异常样本增广。尽管仅含150万可训练参数，DROID在多个意图基准上持续优于近期最先进方法，对已知意图宏F1提升6–15%，对OOS意图提升8–20%，尤其在低资源设置下表现突出。结果表明，双编码器表示搭配简单校准即可实现神经对话系统中稳健、可扩展且可靠的OOS检测。
原文摘要 · Abstract (English)
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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