---
title: "Dynamic Coattention Networks For Question — Xiong 2016"
description: "The paper targets extractive question answering on SQuAD, where the answer is a contiguous span of a document."
authors: ["Caiming Xiong", "Victor Zhong", "Richard Socher"]
year: 2016
venue: "arXiv:1611.01604"
source: "https://arxiv.org/abs/1611.01604"
doi: "10.48550/arxiv.1611.01604"
arxiv: "1611.01604"
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/xiong-2016-dynamic-coattention-networks-question-answering/"
---
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# Dynamic Coattention Networks For Question Answering

**Authors:** [Caiming Xiong](https://md.research.elamur.ai/free/en/authors/caiming-xiong/), [Victor Zhong](https://md.research.elamur.ai/free/en/authors/victor-zhong/), [Richard Socher](https://md.research.elamur.ai/free/en/authors/richard-socher/) · **Year:** 2016 · **Venue:** arXiv:1611.01604 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1611.01604) · [PDF](https://arxiv.org/pdf/1611.01604) · [DOI 10.48550/arxiv.1611.01604](https://doi.org/10.48550/arxiv.1611.01604) · [arXiv 1611.01604](https://arxiv.org/abs/1611.01604) · [OpenAlex](https://openalex.org/W2552027021)

**Citations:** 450 citations (openalex-snapshot · 2026-10-14) · [128](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper targets extractive question answering on SQuAD, where the answer is a contiguous span of a document. Earlier neural readers choose the span in a single pass, so an early wrong guess cannot be corrected. The authors propose the Dynamic Coattention Network (DCN), which has two parts. The first is a coattention encoder. A shared LSTM encodes both question and document, and an affinity matrix over all word pairs lets each text attend to the other. The resulting attention contexts are combined and passed through a bidirectional LSTM. The second part is a dynamic pointing decoder. It repeatedly re-estimates the start and end positions using an LSTM state and Highway Maxout Networks, which lets the model escape poor initial span choices. As a single model, DCN raised the best published SQuAD F1 from 71.0% to 75.9%. An ensemble reached 80.4% F1. This showed that refining answers iteratively and modeling question–document interaction jointly yield large gains in reading comprehension.

## Key points
- **Encoder:** The question and the document share one LSTM encoder, and each sequence gets a sentinel vector so the model can avoid attending to any specific word. The question encoding receives an extra non-linear projection.
- **Coattention:** Attention weights are normalized row-wise and column-wise over the affinity matrix. Summaries are computed in both directions, including attention over the earlier attention contexts, and then fused with the document encoding by a bi-LSTM.
- **Decoder:** Separate Highway Maxout Networks (HMN start, HMN end) score start and end positions. Training minimizes cross-entropy accumulated over all iterations. Decoding stops once the predictions no longer change or a maximum iteration count is reached.
- **Results:** The single model scored 75.9% F1 versus 71.0% for Yu et al. (2016). The ensemble scored 80.4% F1, while the next-best entry on the SQuAD leaderboard (as of Nov. 3, 2016) had 78.1%.
- **Error analysis:** Wrong answers usually still have the right type (e.g., a person), even though no type information is given to the model.

*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: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

128 papers of our library cite it (of the 140884 whose bibliographies we read); 16 of them have a page here, 112 not yet.

- [Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering](https://md.research.elamur.ai/free/en/papers/asai-2019-learning-retrieve-reasoning-paths-over/)
- [Reading Wikipedia to Answer Open-Domain Questions](https://md.research.elamur.ai/free/en/papers/chen-2017-reading-wikipedia-answer-open-domain/)
- [A Span-Extraction Dataset for Chinese Machine Reading Comprehension](https://md.research.elamur.ai/free/en/papers/cui-2018-span-extraction-dataset-chinese/)
- [Question Generation for Question Answering](https://md.research.elamur.ai/free/en/papers/duan-2017-question-generation-question-answering/)
- [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://md.research.elamur.ai/free/en/papers/he-2017-dureader-chinese-machine-reading/)
- [Attention is not Explanation](https://md.research.elamur.ai/free/en/papers/jain-2019-attention-is-not-explanation/)
- [Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds](https://md.research.elamur.ai/free/en/papers/labutov-2018-multi-relational-question-answering-narratives/)
- [A Unified MRC Framework for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/li-2020-unified-mrc-framework-named-entity/)
- [Learned in Translation: Contextualized Word Vectors](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
- [DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension](https://md.research.elamur.ai/free/en/papers/saha-2018-duorc-complex-language-understanding-paraphrased/)
- [Bidirectional Attention Flow for Machine Comprehension](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/)
- [Reasoning with Sarcasm by Reading In-Between](https://md.research.elamur.ai/free/en/papers/tay-2018-reasoning-sarcasm-reading-between/)
- [Gated Self-Matching Networks for Reading Comprehension and Question Answering](https://md.research.elamur.ai/free/en/papers/wang-2017-gated-self-matching-networks-reading/)
- [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://md.research.elamur.ai/free/en/papers/welbl-2018-constructing-datasets-multi-hop-reading/)
- [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/)

## Cite

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

```bibtex
@misc{xiong2016dynamic,
  title = {{Dynamic Coattention Networks For Question Answering}},
  author = {Xiong, Caiming and Zhong, Victor and Socher, Richard},
  year = {2016},
  doi = {10.48550/arxiv.1611.01604},
  eprint = {1611.01604},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1611.01604}
}
```

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## References

28 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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