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WT5?! Training Text-to-Text Models to Explain their Predictions

Paper · 2020 · Sharan Narang, Colin Raffel, Katherine Lee et al.

Summary

Large pre-trained neural models now match or beat human accuracy on many NLP tasks, but they reveal little about why they reach a given decision. The authors present WT5 ("Why, T5?"). It reuses the text-to-text format of T5 so the model writes a natural-language justification right after its label. The loss, training loop and decoding stay unchanged. The input gets the prefix "explain", and the target becomes the label followed by "explanation:" and the rationale. Dropping the prefix yields plain classification, so examples with and without rationales can be mixed in one training run. For extractive benchmarks such as ERASER, generated rationales are mapped back to spans of the input. The largest variant, WT5-11B, achieves state-of-the-art results on explainability benchmarks such as e-SNLI and CoS-E, often approaching human quality. It also learns from a small pool of annotated explanations and carries explanation skills over to new domains and even new tasks. The work offers a simple, general recipe for self-explaining models. However, the authors caution that the explanations need not reflect the model's actual causal reasoning.

Key points

  • Built on pre-trained T5 checkpoints (trained on Common Crawl text, SentencePiece subword tokens). The experiments use the "Base" model (about 220 million parameters) and the "11B" model (about 11 billion parameters).
  • Datasets include e-SNLI (NLI with human-written rationales), CoS-E (commonsense multiple-choice QA), Movie Reviews and the extractive ERASER suite. The authors argue that tasks like the Winograd Schema Challenge (WSC) cannot be handled by span-only explanations.
  • In a semi-supervised setup, the training data is fully labeled but only a fraction of examples carry explanations. Training uses both kinds of examples together.
  • Explanation ability transferred across domains (Movie Reviews → Amazon Reviews). It also transferred zero-shot across tasks (e-SNLI → CoS-E). The cross-task transfer worked only after aligning input formats (e.g., recasting CoS-E fields as "nli"/"premise:" and listing the three NLI labels as choices), and beam search made explanation generation more reliable.
  • Training code is released in the google-research GitHub repository under wt5.

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 States492 works · 440,462 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

Cite

Cite as Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel and Karishma Malkan. 2020. WT5?! Training Text-to-Text Models to Explain their Predictions. arXiv:2004.14546.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2004.14546 · arXiv 2004.14546

@misc{narang2020wt5,
  title = {{WT5?! Training Text-to-Text Models to Explain their Predictions}},
  author = {Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel and Karishma Malkan},
  year = {2020},
  doi = {10.48550/arxiv.2004.14546},
  eprint = {2004.14546},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2004.14546}
}

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