# Out-of-Domain Detection for Low-Resource Text Classification Tasks

arXiv: https://arxiv.org/abs/1909.05357 · HTML: https://mlsanity.com/paper/1909.05357/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 30 of 36)

- [Delving into Out-of-Distribution Detection with Vision-Language Representations](https://mlsanity.com/paper/2211.13445/) (2022)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [LLM Internal States Reveal Hallucination Risk Faced With a Query](https://mlsanity.com/paper/2407.03282/) (2024)
- [Types of Out-of-Distribution Texts and How to Detect Them](https://mlsanity.com/paper/2109.06827/) (2021)
- [Contrastive Out-of-Distribution Detection for Pretrained Transformers](https://mlsanity.com/paper/2104.08812/) (2021)
- [Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection](https://mlsanity.com/paper/2101.03778/) (2021)
- [Uncertainty in Natural Language Processing: Sources, Quantification, and Applications](https://mlsanity.com/paper/2306.04459/) (2023)
- [Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training](https://mlsanity.com/paper/2106.08616/) (2021)
- [A Survey on Out-of-Distribution Detection in NLP](https://mlsanity.com/paper/2305.03236/) (2023)
- [Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection](https://mlsanity.com/paper/2305.13282/) (2023)
- [Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes](https://mlsanity.com/paper/2104.08481/) (2021)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity](https://mlsanity.com/paper/2210.15452/) (2022)
- [Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU](https://mlsanity.com/paper/2106.14464/) (2021)
- [GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation](https://mlsanity.com/paper/2109.03079/) (2021)
- [Towards Textual Out-of-Domain Detection without In-Domain Labels](https://mlsanity.com/paper/2203.11396/) (2022)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [Estimating Soft Labels for Out-of-Domain Intent Detection](https://mlsanity.com/paper/2211.05561/) (2022)
- [SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation](https://mlsanity.com/paper/2406.12629/) (2024)
- [Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization](https://mlsanity.com/paper/2109.07968/) (2021)
- [Novel Intent Detection and Active Learning Based Classification (Student Abstract)](https://mlsanity.com/paper/2304.11058/) (2023)
- [Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts](https://mlsanity.com/paper/2305.03237/) (2023)
- [Learning to Classify Open Intent via Soft Labeling and Manifold Mixup](https://mlsanity.com/paper/2204.07804/) (2022)
- [An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification](https://mlsanity.com/paper/2409.03203/) (2024)
- [ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection](https://mlsanity.com/paper/2108.12229/) (2021)
- [Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification](https://mlsanity.com/paper/2308.02746/) (2023)
- [Towards Confident Machine Reading Comprehension](https://mlsanity.com/paper/2101.07942/) (2021)
- [A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection](https://mlsanity.com/paper/2205.02005/) (2022)
- [Can Humans Identify Domains?](https://mlsanity.com/paper/2404.01785/) (2024)
- [Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks](https://mlsanity.com/paper/2205.09021/) (2022)

## Abstract (arXiv)

Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training data, since we observe that training data is often insufficient in machine learning applications. In this work, we propose an OOD-resistant Prototypical Network to tackle this zero-shot OOD detection and few-shot ID classification task. Evaluation on real-world datasets show that the proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task, while maintaining a competitive performance on ID classification task.