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Did the Model Understand the Question?

Paper · 2018 · Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan et al.

Summary

The paper asks whether high-accuracy question answering networks actually use the meaningful words in a question. The authors apply Integrated Gradients (IG) to pre-trained models for three tasks, using an empty question as the attribution baseline. The models are a visual QA network (Kazemi and Elqursh, 2017), Neural Programmer for questions over tables, and the reading comprehension model of Yu et al. (2018). The attributions show that these systems often give little weight to key terms such as nouns and adjectives. Instead, they lean on generic or content-free words, or on the context itself.

The authors use these weak spots to build targeted adversarial questions. They also propose an overstability test that tracks how accuracy changes as words are removed from questions. Their attacks sharply degrade the visual and table models, and attribution insights make the ADDSENT attacks of Jia and Liang (2017) more effective.

The work matters because test-set accuracy alone can hide models that are right for the wrong reasons. Attributions help expose this and can point toward better test data.

Key points

  • Adding meaningless prefixes to questions cuts accuracy sharply:
    • the visual QA model falls from 61.1% to 19%;
    • Neural Programmer falls from 33.5% to 3.3%.
  • The visual QA model keeps over half of its accuracy even when every question word except "color" is removed.
  • Neural Programmer's operation choices are driven more by function words than by content words.
    • Dropping the function words only lowers its accuracy to 28.5%.
    • On the perturbed tables of Pasupat and Liang (2016), its accuracy falls to 23%.
  • The authors analyzed 1000 ADDSENT cases, of which 508 fooled the model.
    • Attacks were more likely to fail when the added sentence omitted a highly attributed question noun or adjective: 63% failed in that group versus 40% otherwise.
  • Code for generating attributions and reproducing the results is released on GitHub (pramodkaushik/acl18_results).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢GoogleMountain View · United States353 works · 364,806 citations🎓University of ChicagoChicago · United States28 works · 49,773 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

Cite

Cite as Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan and Kedar Dhamdhere. 2018. Did the Model Understand the Question?. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/p18-1176

@inproceedings{mudrakarta2018did,
  title = {{Did the Model Understand the Question?}},
  author = {Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan and Kedar Dhamdhere},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2018},
  doi = {10.18653/v1/p18-1176},
  url = {https://doi.org/10.18653/v1/p18-1176}
}

References

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