---
title: "Back to the Future — paper by Qin et al. (2020)"
description: "The authors propose DELOREAN, a decoding method for logical reasoning of the nonmonotonic kind… 57 references, cited by 59 papers here."
authors: ["Lianhui Qin", "Vered Shwartz", "Peter West", "Chandra Bhagavatula", "Jena D. Hwang", "Ronan Le Bras", "Antoine Bosselut", "Yejin Choi"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.58/"
doi: "10.18653/v1/2020.emnlp-main.58"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/qin-2020-back-future-unsupervised-backprop-based/"
---
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# Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning

**Authors:** [Lianhui Qin](https://md.research.elamur.ai/free/en/authors/lianhui-qin/), [Vered Shwartz](https://md.research.elamur.ai/free/en/authors/vered-shwartz/), [Peter West](https://md.research.elamur.ai/free/en/authors/peter-west/), [Chandra Bhagavatula](https://md.research.elamur.ai/free/en/authors/chandra-bhagavatula/), [Jena D. Hwang](https://md.research.elamur.ai/free/en/authors/jena-d-hwang/), [Ronan Le Bras](https://md.research.elamur.ai/free/en/authors/ronan-le-bras/), [Antoine Bosselut](https://md.research.elamur.ai/free/en/authors/antoine-bosselut/) and [Yejin Choi](https://md.research.elamur.ai/free/en/authors/yejin-choi/) · **Year:** 2020 · **Venue:** [Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2020](https://md.research.elamur.ai/free/en/venues/emnlp/2020/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.58/) · [PDF](https://aclanthology.org/2020.emnlp-main.58.pdf) · [DOI 10.18653/v1/2020.emnlp-main.58](https://doi.org/10.18653/v1/2020.emnlp-main.58) · [OpenAlex](https://openalex.org/W3103451137)

**Citations:** 27 citations (OpenAlex, 2026-10-14) · [59](https://md.research.elamur.ai/free/en/papers/qin-2020-back-future-unsupervised-backprop-based/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary

Abductive reasoning asks what plausibly happened between an observed past and an observed future. Counterfactual reasoning asks how a story's later events should change when an earlier event is altered. Both require a model to condition on context on both sides of the text it generates. Standard left-to-right language models such as GPT2 only see the preceding context, and text-infilling models handle only short spans.

The authors propose DELOREAN, a decoding method for logical reasoning of the nonmonotonic kind (its name is an acronym formed from those words). It requires no supervision and works with an off-the-shelf left-to-right LM:

- A forward pass produces output logits from the past context.
- A backward pass back-propagates a task-specific loss tied to the future constraint. It adjusts only the output representation, not the model weights.
- The two sets of logits are blended, and the cycle repeats.
- Candidates from the iterations are ranked for coherence with BERT's next sentence prediction.

The method is evaluated on the ART dataset (abductive NLG) and the TIME TRAVEL dataset (counterfactual story rewriting). DELOREAN beats all unsupervised baselines in automatic and human evaluation, and its outputs are judged more coherent than those of some supervised systems. This suggests pretrained LMs can perform nonmonotonic reasoning without task-specific training data.

## Key points

- **How it works:** DELOREAN alternates forward passes, which roughly favour P(Y|X), with backward passes, which roughly push toward P(Z|XY). Parameters of the pretrained LM (GPT2) stay frozen throughout.
- **Benchmarks:** ART (about 20k observation pairs taken from ROCStories) and TIME TRAVEL (five-sentence stories with a counterfactual change to the second sentence).
- **Counterfactual results:** DELOREAN balances coherence and minimal edits. It is more coherent than discriminative baselines (Recon+CF, Sup+Disc) and needs fewer edits than zero-shot re-ranked baselines. Recon+CF copied the original ending verbatim in 84% of cases.
- **Human evaluation:** inter-rater agreement was fair, with Fleiss' κ = 0.34.
- **Code:** released at github.com/qkaren/unsup_gen_for_cms_reasoning.

*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 Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) — University, Seattle, United States · 263 works · 180,581 citations
- [Allen Institute for Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/allen-institute-for-artificial-intelligence/) — Research institute, Seattle, United States · 219 works · 89,712 citations

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## Cite

Cite as: Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D. Hwang, Ronan Le Bras, Antoine Bosselut and Yejin Choi. 2020. Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning. 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.

```bibtex
@inproceedings{qin2020back,
  title = {{Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning}},
  author = {Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D. Hwang, Ronan {Le Bras}, Antoine Bosselut and Yejin Choi},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.58},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.58}
}
```

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*Updated 24 October 2026*