Abstract
Machine learning systems have shown impressive performance across a range of natural language tasks. However, it has been hypothesized that these systems are prone to learning spurious correlations that may be present in the training data. Though these correlations will not impact in-domain performance, they are unlikely to generalize well to out-of-domain data, limiting the applicability of systems. This work examines this phenomenon on text classification tasks. Rather than artificially injecting features into the data, we demonstrate that real spurious correlations can be exploited by current state-of-the-art deep-learning systems. Specifically, we show that even when only ‘stop’ words are available at the input stage, it is possible to predict the class significantly better than random. Though it is shown that these stop words are not required for good in-domain performance, they can degrade the ability of the system to generalize well to out-of-domain data.- Anthology ID:
- 2022.aacl-short.11
- Volume:
- Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2022
- Address:
- Online only
- Editors:
- Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
- Venues:
- AACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 78–84
- Language:
- URL:
- https://aclanthology.org/2022.aacl-short.11
- DOI:
- Cite (ACL):
- Adian Liusie, Vatsal Raina, Vyas Raina, and Mark Gales. 2022. Analyzing Biases to Spurious Correlations in Text Classification Tasks. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 78–84, Online only. Association for Computational Linguistics.
- Cite (Informal):
- Analyzing Biases to Spurious Correlations in Text Classification Tasks (Liusie et al., AACL-IJCNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.aacl-short.11.pdf