Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing
Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon, Yangfeng Ji, Yanjun Qi
Abstract
Interpretability methods like Integrated Gradient and LIME are popular choices for explaining natural language model predictions with relative word importance scores. These interpretations need to be robust for trustworthy NLP applications in high-stake areas like medicine or finance. Our paper demonstrates how interpretations can be manipulated by making simple word perturbations on an input text. Via a small portion of word-level swaps, these adversarial perturbations aim to make the resulting text semantically and spatially similar to its seed input (therefore sharing similar interpretations). Simultaneously, the generated examples achieve the same prediction label as the seed yet are given a substantially different explanation by the interpretation methods. Our experiments generate fragile interpretations to attack two SOTA interpretation methods, across three popular Transformer models and on three different NLP datasets. We observe that the rank order correlation and top-K intersection score drops by over 20% when less than 10% of words are perturbed on average. Further, rank-order correlation keeps decreasing as more words get perturbed. Furthermore, we demonstrate that candidates generated from our method have good quality metrics.- Anthology ID:
- 2021.blackboxnlp-1.33
- Volume:
- Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Jasmijn Bastings, Yonatan Belinkov, Emmanuel Dupoux, Mario Giulianelli, Dieuwke Hupkes, Yuval Pinter, Hassan Sajjad
- Venue:
- BlackboxNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 420–434
- Language:
- URL:
- https://aclanthology.org/2021.blackboxnlp-1.33
- DOI:
- 10.18653/v1/2021.blackboxnlp-1.33
- Cite (ACL):
- Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon, Yangfeng Ji, and Yanjun Qi. 2021. Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing. In Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 420–434, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing (Sinha et al., BlackboxNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.blackboxnlp-1.33.pdf
- Code
- qdata/textattack-fragile-interpretations
- Data
- AG News, IMDb Movie Reviews, SST, SST-2