A Critical Analysis of Document Out-of-Distribution Detection
Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova
Abstract
Large-scale pre-training is widely used in recent document understanding tasks. During deployment, one may expect that models should trigger a conservative fallback policy when encountering out-of-distribution (OOD) samples, which highlights the importance of OOD detection. However, most existing OOD detection methods focus on single-modal inputs such as images or texts. While documents are multi-modal in nature, it is underexplored if and how multi-modal information in documents can be exploited for OOD detection. In this work, we first provide a systematic and in-depth analysis on OOD detection for document understanding models. We study the effects of model modality, pre-training, and fine-tuning across various types of OOD inputs. In particular, we find that spatial information is critical for document OOD detection. To better exploit spatial information, we propose a spatial-aware adapter, which serves as a parameter-efficient add-on module to adapt transformer-based language models to the document domain. Extensive experiments show that adding the spatial-aware adapter significantly improves the OOD detection performance compared to directly using the language model and achieves superior performance compared to competitive baselines.- Anthology ID:
- 2023.findings-emnlp.332
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4973–4999
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.332
- DOI:
- 10.18653/v1/2023.findings-emnlp.332
- Cite (ACL):
- Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, and Ani Nenkova. 2023. A Critical Analysis of Document Out-of-Distribution Detection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4973–4999, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- A Critical Analysis of Document Out-of-Distribution Detection (Gu et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2023.findings-emnlp.332.pdf