---
title: "Open-Domain Question Answering Goes — Anantha 2021"
description: "Existing conversational QA benchmarks such as QuAC and CoQA confine each dialogue to a single document."
authors: ["Raviteja Anantha", "Svitlana Vakulenko", "Zhucheng Tu", "Shayne Longpre", "Stephen Pulman", "Srinivas Chappidi"]
year: 2021
venue: "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/2021.naacl-main.44/"
doi: "10.18653/v1/2021.naacl-main.44"
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/anantha-2021-open-domain-question-answering-goes/"
---
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# Open-Domain Question Answering Goes Conversational via Question Rewriting

**Authors:** Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, [Shayne Longpre](https://md.research.elamur.ai/free/en/authors/shayne-longpre/), [Stephen Pulman](https://md.research.elamur.ai/free/en/authors/stephen-pulman/), Srinivas Chappidi · **Year:** 2021 · **Venue:** Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.naacl-main.44/) · [PDF](https://aclanthology.org/2021.naacl-main.44.pdf) · [DOI 10.18653/v1/2021.naacl-main.44](https://doi.org/10.18653/v1/2021.naacl-main.44) · [OpenAlex](https://openalex.org/W3092307876)

**Citations:** 123 citations (OpenAlex, 2026-10-14) · [104](https://md.research.elamur.ai/free/en/papers/anantha-2021-open-domain-question-answering-goes/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary

Existing conversational QA benchmarks such as QuAC and CoQA confine each dialogue to a single document. In real use, a conversation can drift between topics and its answers may sit on many different web pages. To address this, the authors built QReCC, an open-domain conversational QA dataset of about 13.6K dialogues and 80K question-answer pairs. Professional annotators wrote each dialogue alone, starting from seed questions taken from QuAC, TREC CAsT and Natural Questions. They searched the web for answers, wrote a self-contained rewrite for every question, and linked each answer to its source page. The answer collection includes these pages plus Common Crawl data, giving roughly 10M pages split into 54M passages.

The baseline handles conversation by first rewriting each question so it stands alone. It then applies a standard open-domain QA pipeline, BERTserini, which combines BM25 retrieval with a BERT reader. The best rewriting model, Transformer++, roughly doubles the end-to-end F1 obtained with the original questions. Even so, it reaches only 19.10 F1, against an extractive upper bound of 75.45. This leaves substantial room for better retrieval and for abstractive answer generation.

## Key points

- **Dataset size and splits:** QReCC has 80.0K questions in 13.6K dialogues, averaging 6 turns. The splits are 50.8K train, 12.7K dev and 16.4K test questions. It is released at github.com/apple/ml-qrecc.
- **Coverage and annotation:** It is the first large-scale resource covering question rewriting, passage retrieval and reading comprehension together. Annotation used a self-dialog setup with 30 professional annotators, followed by a 5-reviewer quality check.
- **Question rewriting results:** All generative rewriting models beat the AllenAI Coref coreference baseline, and Transformer++ performed best. Retrieval MRR was 0.0343 with original questions, 0.1586 with Transformer++ rewrites and 0.1994 with human rewrites.
- **End-to-end QA results (F1):** Original questions scored 9.07, Transformer++ rewrites 19.10 and human rewrites 21.82. Given the gold passage ("Known Context"), Transformer++ reaches 32.34 F1.
- **Upper bound and answer style:** The extractive upper bound is 75.45 F1 (25.07 EM). Some reference answers paraphrase or combine content from several passages, so span extraction alone cannot fully match them.

*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: [Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/)

## Institutions

- [University of Amsterdam](https://md.research.elamur.ai/free/en/universities/university-of-amsterdam/) — University, Amsterdam, Netherlands · 71 works · 79 805 citations
- [Apple (Israel)](https://md.research.elamur.ai/free/en/companies/apple-israel/) — Company, Herzliya, Israel · 3 works · 789 citations

## Cite

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

```bibtex
@inproceedings{anantha2021open,
  title = {{Open-Domain Question Answering Goes Conversational via Question Rewriting}},
  author = {Anantha, Raviteja and Vakulenko, Svitlana and Tu, Zhucheng and Longpre, Shayne and Pulman, Stephen and Chappidi, Srinivas},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.44},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.44}
}
```

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