---
title: "Learning from Task Descriptions — paper by Weller et al. (2020)"
description: "The authors propose \"learning from task descriptions\" as a general framework for this. 90% F1. 42 references, cited by 53 papers here."
authors: ["Orion Weller", "Nicholas Lourie", "Matt Gardner", "Matthew E. Peters"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.105/"
doi: "10.18653/v1/2020.emnlp-main.105"
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/weller-2020-learning-task-descriptions/"
---
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# Learning from Task Descriptions

**Authors:** [Orion Weller](https://md.research.elamur.ai/free/en/authors/orion-weller/), [Nicholas Lourie](https://md.research.elamur.ai/free/en/authors/nicholas-lourie/), [Matt Gardner](https://md.research.elamur.ai/free/en/authors/matt-gardner/) and [Matthew E. Peters](https://md.research.elamur.ai/free/en/authors/matthew-peters/) · **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.105/) · [PDF](https://aclanthology.org/2020.emnlp-main.105.pdf) · [DOI 10.18653/v1/2020.emnlp-main.105](https://doi.org/10.18653/v1/2020.emnlp-main.105) · [OpenAlex](https://openalex.org/W3099338292)

**Citations:** 58 citations (OpenAlex, 2026-10-14) · [53](https://md.research.elamur.ai/free/en/papers/weller-2020-learning-task-descriptions/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Most NLP systems learn a new task only after seeing thousands of labeled examples. Humans can often do the same task just by reading instructions. The authors propose "learning from task descriptions" as a general framework for this. A model receives both an input and a natural-language description of the task, and it must handle tasks it never saw during training. The framework unifies earlier zero-shot work on classification, entity typing and relation extraction, and extends it to slot filling.

To test the idea, they built ZEST, an English dataset in reading-comprehension style. Each task is phrased as a question and paired with twenty passages. Scoring is per task: a new metric, C@T, counts a task as solved only if performance across all its inputs reaches threshold T. Variants of the questions probe specific kinds of systematic generalization.

The strongest baseline, T5-11B, solves just 12% of test tasks at 90% F1, while humans are estimated at 42%. This shows that zero-shot learning from instructions remains far from solved.

## Key points
- **ZEST** (ZEro Shot learning from Task descriptions) pairs every question-style task description with 20 passages. It tests generalization across four axes beyond a Base level: Paraphrase, Composition, Semantic Flips and Output Structure. Output Structure tasks require structured (JSON) answers.
- **Competence metric C@T:** the share of tasks on which a model's per-task score meets threshold T. Results are reported for T = 75 and T = 90.
- **Results:** T5-11B reaches about 56% mean F1 on dev but only 12% C@90 on test. BART scores lower. Adding multi-task training on other QA datasets helped only marginally, and accuracy falls as generalization difficulty rises.
- **Ablations and errors:**
  - A context-only model just outputs NA, so passages alone contain no exploitable artifacts.
  - Among 100 analyzed errors, 37% were answers given where NA was correct, and 30% were missed answers.
  - Invalid JSON appeared in only 1.5% of structure questions.
- **Availability:** data, evaluation code, baselines and a leaderboard are released at allenai.org/data/zest.

*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

- [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
- [Brigham Young University](https://md.research.elamur.ai/free/en/universities/brigham-young-university/) — University, Provo, United States · 15 works · 40,065 citations

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## Cite

Cite as: Orion Weller, Nicholas Lourie, Matt Gardner and Matthew E. Peters. 2020. Learning from Task Descriptions. 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{weller2020learning,
  title = {{Learning from Task Descriptions}},
  author = {Orion Weller, Nicholas Lourie, Matt Gardner and Matthew E. Peters},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.105},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.105}
}
```

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*Updated 24 October 2026*