Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations

Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, Phil Blunsom


Abstract
To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for their predictions. In this work, we show that such models are nonetheless prone to generating mutually inconsistent explanations, such as ”Because there is a dog in the image.” and ”Because there is no dog in the [same] image.”, exposing flaws in either the decision-making process of the model or in the generation of the explanations. We introduce a simple yet effective adversarial framework for sanity checking models against the generation of inconsistent natural language explanations. Moreover, as part of the framework, we address the problem of adversarial attacks with full target sequences, a scenario that was not previously addressed in sequence-to-sequence attacks. Finally, we apply our framework on a state-of-the-art neural natural language inference model that provides natural language explanations for its predictions. Our framework shows that this model is capable of generating a significant number of inconsistent explanations.
Anthology ID:
2020.acl-main.382
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4157–4165
Language:
URL:
https://aclanthology.org/2020.acl-main.382
DOI:
10.18653/v1/2020.acl-main.382
Bibkey:
Cite (ACL):
Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, and Phil Blunsom. 2020. Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4157–4165, Online. Association for Computational Linguistics.
Cite (Informal):
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations (Camburu et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.382.pdf
Video:
 http://slideslive.com/38928826
Code
 OanaMariaCamburu/e-SNLI
Data
SNLIe-SNLI