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End-to-End Bias Mitigation by Modelling Biases in Corpora

Summary

Strong NLU models often succeed on benchmarks by exploiting spurious cues in the data. In NLI, for example, negation words in the hypothesis tend to signal contradiction, so such models break down on test sets built without those artifacts. The authors offer two training-time remedies for cases where the bias is known in advance. A separate bias-only model, such as a hypothesis-only classifier for NLI or a model using syntactic heuristics and similarity features for HANS, is trained jointly with the main model. Its predictions reshape the main model's loss so that examples the shortcut already solves count less, and learning shifts toward the hard cases.

The two variants are a Product of Experts (PoE) ensemble and a Debiased Focal Loss (DFL). Both train end-to-end, work with any architecture, and can handle several bias sources at once. Tested on SNLI, MNLI and FEVER with challenge sets such as HANS, they clearly improve out-of-domain accuracy and transfer to other entailment data. This makes models more reliable without the cost of collecting new datasets.

Key points

  • How the methods work:
    • PoE adds the log-probabilities of the bias-only model and the base model before computing the loss.
    • DFL uses the bias-only model's confidence in the correct label to directly scale down the base model's loss.
    • The bias-only loss is never back-propagated into parameters it shares with the base model, and the bias branch is discarded after training.
  • Headline gains: the methods reached a new state of the art, improving on the baselines by 7.4 points on HANS, 4.8 points on the SNLI hard set and 9.8 points on the FEVER Symmetric test set. The authors also built hard MNLI subsets from the MNLI development sets.
  • Transfer results: BERT models trained on SNLI were evaluated on 12 target datasets. Both debiased versions beat the cross-entropy baseline on every target except MNLI and MNLI-M, whose biases resemble SNLI's. The largest gain was on SciTail, with +3.8 for PoE and +1.7 for DFL.
  • Correlation analysis: the losses of the debiased models correlate less with the bias-only model's loss than the baseline's losses do. PoE showed the lower correlation on MNLI, while DFL did so on HANS.
  • Trade-off and availability: DFL's γ parameter balances in-domain against out-of-domain accuracy, which matters especially for weaker encoders such as InferSent. Code and data are available at github.com/rabeehk/robust-nli.

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

Fields: Natural language processing

Institutions

🎓Massachusetts Institute of TechnologyCambridge · United States125 works · 60 031 citations🎓Harvard UniversityCambridge · United States64 works · 35 670 citations🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland19 works · 6 324 citations🔬Idiap Research InstituteMartigny-Combe · Switzerland17 works · 1 669 citations

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.acl-main.769

@inproceedings{mahabadi2020end,
  title = {{End-to-End Bias Mitigation by Modelling Biases in Corpora}},
  author = {Mahabadi, Rabeeh Karimi and Belinkov, Yonatan and Henderson, James},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.769},
  url = {https://doi.org/10.18653/v1/2020.acl-main.769}
}

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