---
title: "Conditional Generation with a Question-Answering Blueprint (2023)"
description: "Neural seq-to-seq generators frequently hallucinate, omit important details, and… 97.87% agreement. 96 references, cited by 46 papers here."
authors: ["Shashi Narayan", "Joshua Maynez", "Reinald Kim Amplayo", "Kuzman Ganchev", "Annie Louis", "Fantine Huot", "Anders Sandholm", "Dipanjan Das", "Mirella Lapata"]
year: 2023
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2023.tacl-1.55/"
doi: "10.1162/tacl_a_00583"
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/narayan-2023-conditional-generation-question-answering-blueprint/"
---
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# Conditional Generation with a Question-Answering Blueprint

**Authors:** [Shashi Narayan](https://md.research.elamur.ai/free/en/authors/shashi-narayan/), [Joshua Maynez](https://md.research.elamur.ai/free/en/authors/joshua-maynez/), [Reinald Kim Amplayo](https://md.research.elamur.ai/free/en/authors/reinald-kim-amplayo/), [Kuzman Ganchev](https://md.research.elamur.ai/free/en/authors/kuzman-ganchev/), [Annie Louis](https://md.research.elamur.ai/free/en/authors/annie-louis/), [Fantine Huot](https://md.research.elamur.ai/free/en/authors/fantine-huot/), [Anders Sandholm](https://md.research.elamur.ai/free/en/authors/anders-sandholm/), [Dipanjan Das](https://md.research.elamur.ai/free/en/authors/dipanjan-das/) and [Mirella Lapata](https://md.research.elamur.ai/free/en/authors/mirella-lapata/) · **Year:** 2023 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2023](https://md.research.elamur.ai/free/en/venues/tacl/2023/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.tacl-1.55/) · [PDF](https://aclanthology.org/2023.tacl-1.55.pdf) · [DOI 10.1162/tacl\_a\_00583](https://doi.org/10.1162/tacl_a_00583) · [OpenAlex](https://openalex.org/W4385859649)

**Citations:** 24 citations (OpenAlex, 2026-10-14) · [46](https://md.research.elamur.ai/free/en/papers/narayan-2023-conditional-generation-question-answering-blueprint/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Neural seq-to-seq generators frequently hallucinate, omit important details, and act as black boxes that are hard to inspect or steer. The authors address this by adding an explicit intermediate plan, drawing on the discourse framework known as Questions under Discussion (QUD). The plan, called a blueprint, is an ordered list of question-answer pairs that encodes both content selection (what to say) and ordering. Existing summarization datasets are enriched automatically: a question generation and answer identification system is applied to reference outputs, which turns (input, output) pairs into (input, blueprint, output) triples. Three Transformer encoder-decoder variants are trained on these triples:
- E2E and MULTITASK produce a global blueprint before the text.
- ITERATIVE alternates between planning and writing, one sentence at a time.

Across three summarization datasets, blueprint models are more factual than unplanned baselines, and QA plans outperform entity-chain plans. Generated outputs can also be traced back to, and edited through, the blueprint. This makes generation more transparent and controllable, and it suits long-form output.

## Key points
- Blueprints are built automatically with the QA generation system of Alberti et al. (2019), using overgeneration followed by filtering. The example in the paper is an AQuaMuSe Wikipedia abstract.
- Four models are compared: E2E, MULTITASK, ITERATIVE, and ITERATIVE+drop, the last of which is prompted with externally modified blueprints.
- In human evaluation (100 instances × 3 datasets × 5 systems × 3 raters = 4,500 judgments per study; 97.87% agreement on blueprint quality), E2E produced the most coherent blueprints.
- ITERATIVE blueprints were the most redundant, but ITERATIVE summaries were the most grounded in their plans: they had the fewest absent or contradicted QA pairs and the least extra information. ITERATIVE+drop improved automatic entailment scores on all three datasets.
- Models, training data, and predictions are released in the google-research GitHub repository under `text_blueprint`.

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

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## Cite

Cite as: Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Fantine Huot, Anders Sandholm, Dipanjan Das and Mirella Lapata. 2023. Conditional Generation with a Question-Answering Blueprint. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{narayan2023conditional,
  title = {{Conditional Generation with a Question-Answering Blueprint}},
  author = {Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Fantine Huot, Anders Sandholm, Dipanjan Das and Mirella Lapata},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.1162/tacl\_a\_00583},
  url = {https://doi.org/10.1162/tacl_a_00583}
}
```

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*Updated 30 October 2026*