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An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction

Paper · 2020 · Bhargavi Paranjape, Mandar Joshi, John Thickstun et al.

Summary

Rationale-based models make predictions from a selected part of the input text, which makes them easier to interpret. They usually lose accuracy compared with models that read the whole input, and norm-based sparsity penalties (L0/L1) make this trade-off hard to tune. The authors reformulate rationale extraction with the Information Bottleneck (IB) principle. They derive a variational bound in which the bottleneck is a binary mask over sentences, not a compressed vector. An explainer (a pre-trained BERT-style transformer) estimates the probability of keeping each sentence. A separate end-task predictor sees only the kept sentences, so its predictions are faithful to the rationale. Sparsity is set by a KL term that pulls the per-sentence Bernoulli mask distribution toward a fixed prior π, which sets the target fraction of retained text. On five ERASER tasks and BeerAdvocate, this "Sparse IB" objective beats norm-minimization baselines in both task scores and agreement with human rationales. It also matches the requested sparsity levels more reliably, giving finer control over the trade-off between conciseness and accuracy.

Key points

  • Compared with norm-minimization methods, Sparse IB gives up to 5% relative gain in task metrics and 6–80% relative gain in agreement with human rationale annotations.
  • Without rationale supervision, the model stays within about 10% of a same-size model that uses the full input. Adding gold masks for roughly 25% of training examples closes this gap for most tasks.
  • The prior π can be estimated from the share of text humans mark as rationale, or tuned directly (assumed to lie in (0, 0.5)). Unlike the ||m||₀ penalty, the KL objective allows sparsity to vary from example to example.
  • The method does not depend on a particular architecture. In the word-level BERT+LSTM setting from ERASER, the Movies task score rises from 91.96 (Sparse Norm-C) to 93.46.
  • Error analysis shows failures when the context contains evidence for both labels, and on shallow lexical matches and confusions requiring world knowledge. Code: github.com/bhargaviparanjape/explainable_qa.

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

🎓University of WashingtonSeattle · United States263 works · 180,581 citations🔬Allen Institute for Artificial IntelligenceSeattle · United States219 works · 89,712 citations🔬Allen InstituteSeattle · United States5 works · 4,252 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 Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi and Luke Zettlemoyer. 2020. An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.emnlp-main.153

@inproceedings{paranjape2020information,
  title = {{An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction}},
  author = {Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi and Luke Zettlemoyer},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.153},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.153}
}

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