---
title: "Open Question Answering over Tables and Text — Chen 2020"
description: "The authors present the first study of open QA that draws on both Wikipedia… 10% exact match. 43 references, cited by 107 papers here."
authors: ["Wenhu Chen", "Ming-Wei Chang", "Eva Schlinger", "William Wang", "William W. Cohen"]
year: 2020
venue: "arXiv:2010.10439"
source: "https://arxiv.org/abs/2010.10439"
doi: "10.48550/arxiv.2010.10439"
arxiv: "2010.10439"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2020-open-question-answering-over-tables/"
---
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# Open Question Answering over Tables and Text

**Authors:** [Wenhu Chen](https://md.research.elamur.ai/free/en/authors/wenhu-chen/), [Ming-Wei Chang](https://md.research.elamur.ai/free/en/authors/ming-wei-chang/), [Eva Schlinger](https://md.research.elamur.ai/free/en/authors/eva-schlinger/), [William Wang](https://md.research.elamur.ai/free/en/authors/william-yang-wang/) and [William W. Cohen](https://md.research.elamur.ai/free/en/authors/william-w-cohen/) · **Year:** 2020 · **Venue:** arXiv:2010.10439 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2010.10439) · [PDF](https://arxiv.org/pdf/2010.10439) · [DOI 10.48550/arxiv.2010.10439](https://doi.org/10.48550/arxiv.2010.10439) · [arXiv 2010.10439](https://arxiv.org/abs/2010.10439) · [OpenAlex](https://openalex.org/W3124424060)

**Citations:** 75 citations (OpenAlex, 2026-10-14) · [107](https://md.research.elamur.ai/free/en/papers/chen-2020-open-question-answering-over-tables/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Open-domain QA systems usually search only free text, yet many facts, especially numeric ones and lists of similar entities, are stored in web tables. The authors present the first study of open QA that draws on both Wikipedia tables and passages. They introduce OTT-QA, a dataset derived from HybridQA. Its questions were rewritten so they make sense without a given context, and further questions were added to reduce bias. Systems must locate the relevant table and text themselves, usually across several reasoning hops. Table hyperlinks are not available at evaluation time.

A standard pipeline performs poorly: an iterative retriever followed by a BERT reader scores below 10% exact match. The paper proposes two fixes:
- **Fusion retrieval.** Entity linking attaches passages to table segments, forming fused blocks that are retrieved in a single pass.
- **Cross-block reader.** A sparse-attention transformer reads the top-K blocks jointly.

Together these raise EM above 27%. The work shows that semi-structured tables are a useful but difficult knowledge source for open QA.

## Key points
- OTT-QA has about 45K human-written questions over all of Wikipedia. It builds on HybridQA, which pairs 72K questions with 13K tables in a closed setting.
- Tables average more than 300 words, which makes the 512-token limit of BERT-style readers a real constraint.
- Hyperlinks between tables and passages are given only in the training split. Dev and test require the model to link evidence itself.
- The baseline (iterative retriever plus single-block BERT reader) reaches under 10% EM. Fusion retrieval combined with the cross-block reader exceeds 27% EM.
- In a dev-set error analysis, 49% of examples failed at retrieval. Of the remaining examples, the reader chose the wrong span in 23%. Errors specific to this dataset come from fusion mistakes and from distracting look-alike tables.

*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

- [Google](https://md.research.elamur.ai/free/en/companies/google/) — Company, Mountain View, United States · 492 works · 440,463 citations
- [University of California, Santa Barbara](https://md.research.elamur.ai/free/en/universities/university-of-california-santa-barbara/) — University, Santa Barbara, United States · 67 works · 10,550 citations

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🧭 Topics (1): [❓ Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) · 487 works

## Cite

Cite as: Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Wang and William W. Cohen. 2020. Open Question Answering over Tables and Text. arXiv:2010.10439.

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

```bibtex
@misc{chen2020open,
  title = {{Open Question Answering over Tables and Text}},
  author = {Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Wang and William W. Cohen},
  year = {2020},
  doi = {10.48550/arxiv.2010.10439},
  eprint = {2010.10439},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2010.10439}
}
```

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*Updated 2 November 2026*