---
title: "QuAC: Question Answering in Context — Choi 2018"
description: "QuAC is a large crowdsourced resource for machine reading comprehension in dialogs where a learner seeks information."
authors: ["Eunsol Choi", "He He", "Mohit Iyyer", "Mark Yatskar", "Wen-tau Yih", "Yejin Choi", "Percy Liang", "Luke Zettlemoyer"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1241/"
doi: "10.18653/v1/d18-1241"
arxiv: "1808.07036"
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/choi-2018-quac-question-answering-context/"
---
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# QuAC: Question Answering in Context

**Authors:** [Eunsol Choi](https://md.research.elamur.ai/free/en/authors/eunsol-choi/), [He He](https://md.research.elamur.ai/free/en/authors/he-he/), [Mohit Iyyer](https://md.research.elamur.ai/free/en/authors/mohit-iyyer/), [Mark Yatskar](https://md.research.elamur.ai/free/en/authors/mark-yatskar/), [Wen-tau Yih](https://md.research.elamur.ai/free/en/authors/wen-tau-yih/), [Yejin Choi](https://md.research.elamur.ai/free/en/authors/yejin-choi/), [Percy Liang](https://md.research.elamur.ai/free/en/authors/percy-liang/), [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1241/) · [PDF](https://aclanthology.org/D18-1241.pdf) · [DOI 10.18653/v1/d18-1241](https://doi.org/10.18653/v1/d18-1241) · [arXiv 1808.07036](https://arxiv.org/abs/1808.07036) · [OpenAlex](https://openalex.org/W2888302696)

**Citations:** 667 citations (openalex-snapshot · 2026-10-14) · [389](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
QuAC is a large crowdsourced resource for machine reading comprehension in dialogs where a learner seeks information. Two Amazon Mechanical Turk workers are paired. A "student" sees only an article's opening paragraph and a section heading from a Wikipedia biography. A "teacher" sees the full section and answers each question by highlighting a text span, or by marking it unanswerable. The teacher also attaches dialog acts that signal yes/no, answerability, and whether the student should pursue the current thread. The student cannot see the evidence, so the questions tend to be exploratory, context-dependent, and elliptical, with less surface overlap with the text than in SQuAD or CoQA. The authors tested several baselines, including BiDAF++ (Clark and Gardner, 2018) augmented with previous dialog turns. Even the strongest version trails human agreement by roughly 20 F1. QuAC therefore offers a demanding benchmark for conversational QA systems that must track discourse history.

## Key points
- **Size:** 13,594 dialogs with 98,407 questions over 8,854 Wikipedia sections. About 20% of questions have no answer, and roughly a quarter are yes/no.
- **Answer format:** Answers are spans of at most 30 tokens. They average over 14 tokens, compared with about 3 tokens in SQuAD and CoQA. Each evaluation question received four extra reference annotations.
- **Dialog acts:** Teachers label three dialog act types: continuation (follow up / maybe / don't), affirmation, and answerability.
- **Best model:** BiDAF++ with 2 turns of context. Its performance reaches the human level for just 60% of individual questions and a mere 5% of full dialogs.
- **Error analysis:** Context-aware models degrade less over later turns and handle small topic shifts better. All models struggle with short answers and large jumps in the text. Data, baselines, and a leaderboard are available at quac.ai.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Speech act theory](https://md.research.elamur.ai/free/en/topics/speech-act-theory/)

## Cited by

Cited by

389 papers of our library cite it (of the 140673 whose bibliographies we read); 32 of them have a page here, 357 not yet.

- [Yi: Open Foundation Models by 01.AI](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
- [Model-based Annotation of Coreference](https://md.research.elamur.ai/free/en/papers/aralikatte-2020-model-based-annotation-coreference/)
- [Efficient Training of Language Models to Fill in the Middle](https://md.research.elamur.ai/free/en/papers/bavarian-2022-efficient-training-language-models-fill/)
- [Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/)
- [Pre-Training with Whole Word Masking for Chinese BERT](https://md.research.elamur.ai/free/en/papers/cui-2019-pre-training-whole-word-masking/)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://md.research.elamur.ai/free/en/papers/cui-2020-revisiting-pre-trained-models-chinese/)
- [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://md.research.elamur.ai/free/en/papers/dasigi-2021-dataset-information-seeking-questions-answers/)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/)
- [English Machine Reading Comprehension Datasets: A Survey](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/)
- [ELI5: Long Form Question Answering](https://md.research.elamur.ai/free/en/papers/fan-2019-eli5-long-form-question-answering/)
- [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension](https://md.research.elamur.ai/free/en/papers/fisch-2019-mrqa-2019-shared-task-evaluating/)
- [Multi-tasking Dialogue Comprehension with Discourse Parsing](https://md.research.elamur.ai/free/en/papers/he-2021-multi-tasking-dialogue-comprehension-discourse/)
- [Inquisitive Question Generation for High Level Text Comprehension](https://md.research.elamur.ai/free/en/papers/ko-2020-inquisitive-question-generation-high-level/)
- ["I'd rather just go to bed": Understanding Indirect Answers](https://md.research.elamur.ai/free/en/papers/louis-2020-i-d-rather-just-go/)
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- [A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
- [PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations](https://md.research.elamur.ai/free/en/papers/qi-2023-pragmaticqa-dataset-pragmatic-question-answering/)
- [Measuring Attribution in Natural Language Generation Models](https://md.research.elamur.ai/free/en/papers/rashkin-2023-measuring-attribution-natural-language-generation/)
- [A guide to the dataset explosion in QA, NLI, and commonsense reasoning](https://md.research.elamur.ai/free/en/papers/rogers-2020-guide-dataset-explosion-qa-nli/)
- [Disentangling Indirect Answers to Yes-No Questions in Real Conversations](https://md.research.elamur.ai/free/en/papers/sanagavarapu-2022-disentangling-indirect-answers-yes-no/)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
- [ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT](https://md.research.elamur.ai/free/en/papers/song-2020-zpr2-joint-zero-pronoun-recovery/)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
- [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
- [DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension](https://md.research.elamur.ai/free/en/papers/sun-2019-dream-challenge-data-set-models/)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/xu-2019-bert-post-training-review-reading/)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/)
- [CREPE: Open-Domain Question Answering with False Presuppositions](https://md.research.elamur.ai/free/en/papers/yu-2023-crepe-open-domain-question-answering/)
- [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://md.research.elamur.ai/free/en/papers/zhang-2018-record-bridging-gap-between-human/)

## Cite

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

```bibtex
@inproceedings{choi2018quac,
  title = {{QuAC: Question Answering in Context}},
  author = {Choi, Eunsol and He, He and Iyyer, Mohit and Yatskar, Mark and Yih, Wen-tau and Choi, Yejin and Liang, Percy and Zettlemoyer, Luke},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1241},
  eprint = {1808.07036},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/d18-1241}
}
```

- [← previous QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/)
- [next → QUD-Based Annotation of Discourse Structure and Information Structure: Tool and Evaluation](https://md.research.elamur.ai/free/en/papers/kuthy-2018-qud-based-annotation-discourse-structure/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/3/)

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