---
title: "The NarrativeQA Reading Comprehension — Kočiský 2018"
description: "NarrativeQA pairs 1,567 long stories (half Gutenberg books, half movie scripts) with plot summaries from Wikipedia; annotators who saw only the summary…"
authors: ["Tomáš Kočiský", "Jonathan Schwarz", "Phil Blunsom", "Chris Dyer", "Karl Moritz Hermann", "Gábor Melis", "Edward Grefenstette"]
year: 2018
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/Q18-1023/"
doi: "10.1162/tacl_a_00023"
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/kocisky-2018-narrativeqa-reading-comprehension-challenge/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2018](https://md.research.elamur.ai/free/en/papers/2018/page/4/) / The NarrativeQA Reading Comprehension Challenge

# The NarrativeQA Reading Comprehension Challenge

**Authors:** Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, [Chris Dyer](https://md.research.elamur.ai/free/en/authors/chris-dyer/), [Karl Moritz Hermann](https://md.research.elamur.ai/free/en/authors/karl-moritz-hermann/), Gábor Melis, Edward Grefenstette · **Year:** 2018 · **Venue:** TACL 6 **Link:** [aclanthology.org/Q18-1023](https://aclanthology.org/Q18-1023/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (paper); the data mixes Gutenberg books, scraped movie scripts and Wikipedia summaries (CC BY-SA) — tests only

[**591** citations](https://openalex.org/works?filter=cites:W2779681640) OpenAlex · 2026-10-14 · [**435** citations in our library](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/) of the 140673 papers whose bibliographies we read · [Transactions of the Association for Computational Linguistics](https://aclanthology.org/volumes/Q18-1/) · [licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/Q18-1023/)
- [PDF](https://aclanthology.org/Q18-1023.pdf)
- [DOI 10.1162/tacl_a_00023](https://doi.org/10.1162/tacl_a_00023)
- [OpenAlex](https://openalex.org/W2779681640)

**Authors (7)**

1. [Tomáš Kočiský](https://openalex.org/A5073581900) [Semantic Scholar](https://www.semanticscholar.org/author/2367821) · h 18 · 6019 citations · 29 works
2. [Jonathan Schwarz](https://openalex.org/A5085990900) [ORCID](https://orcid.org/0000-0003-1825-3826) · [Semantic Scholar](https://www.semanticscholar.org/author/144735987) · h 11 · 1750 citations · 18 works
3. [Phil Blunsom](https://openalex.org/A5036811894) [Semantic Scholar](https://www.semanticscholar.org/author/1685771) · h 44 · 14303 citations · 123 works
4. [Chris Dyer](https://openalex.org/A5111222692) [Semantic Scholar](https://www.semanticscholar.org/author/1745899) · h 73 · 36419 citations · 271 works
5. [Karl Moritz Hermann](https://openalex.org/A5001284503) [Semantic Scholar](https://www.semanticscholar.org/author/2910877) · h 25 · 7477 citations · 43 works
6. [Gábor Melis](https://openalex.org/A5039107666) [ORCID](https://orcid.org/0009-0000-3976-9122) · [Semantic Scholar](https://www.semanticscholar.org/author/94303026) · h 9 · 914 citations · 23 works
7. [Edward Grefenstette](https://openalex.org/A5023508792) [ORCID](https://orcid.org/0000-0003-1164-8809) · [Semantic Scholar](https://www.semanticscholar.org/author/1864353) · h 31 · 12266 citations · 109 works

Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart.

## What it is about

NarrativeQA pairs 1,567 long stories (half Gutenberg books, half movie scripts) with plot summaries from Wikipedia; annotators who saw only the summary wrote about 30 question–answer pairs per story in their own words (46,765 in all, answers 4.7 tokens on average, a second reference answer per question). Because the questions come from summaries, answering from the full story requires finding and integrating scattered passages, not matching one sentence. Question first words: what 38%, who 23%, why 10%, how 9%, where 8%; about 15% ask what happened before, after or together with an event, and many ask relations between characters («How is Oscar related to Dana?»). On summaries, span and attention models beat IR baselines but stay far below humans; on full stories, a retrieve-then-read pipeline over 200-word chunks performs only slightly above simple baselines. Entities are replaced by permuted markers so models cannot learn specific names.

## Ideas

- I1. A name-permutation control for the SLM student, after the paper's entity markers: rename the characters of a tale consistently (fox → Ziv, crow → Pell) in both lesson and questions; the student's score must stay the same, because it should read rather than recall. A drop shows reliance on stored knowledge about animals or names. Cheap, deterministic, and a gate that can go red.
- I2. Summary-written questions as a harder test for the books course: let the teacher write a short summary of a tale and questions from the summary only (silver, for measuring), then ask the student on the full tale text. These questions use the summary's words, not the tale's, which tests the frame matching across paraphrase that `world knowledge diagnose` step 2 («nothing remembered matched») counts. Report it separately from the existing courses.
- I3. Character-relation questions («How is the miller related to the princess?», «Who is X's father?») are a frequent type here; check how many such questions the courses have and whether `world/src/family.rs` relations are filled from tale text (appositions, «his daughter», «the king's son») before adding rules.
- I4. For long books, retrieve before reading by frames, not by word chunks: select the frames whose entities and verb lemmas the question shares, in story order, and answer from them; the paper's chunk retrieval was the weak step. Measure on the books course whether answers come from the right part of the story (the decision trace names the frame).

## For Mova

[languages/en/35-reference-and-coreference.md](https://mova.elamur.ai/free/en/docs/languages/en/35-reference-and-coreference/) (characters across a story), `36-discourse-relations.md` §8 (narrative structure). Code: `world/src/knowledge.rs`, `world/src/family.rs`, `world/src/bookans.rs`, `world/src/index.rs`. Measures: score difference under name permutation (should be 0), accuracy on summary-written questions, diagnosis step shares on the books course.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/tacl_a_00023](https://doi.org/10.1162/tacl_a_00023)

```bibtex
@article{kocisku2018narrativeqa,
  title = {{The NarrativeQA Reading Comprehension Challenge}},
  author = {Kočiský, Tomáš and Schwarz, Jonathan and Blunsom, Phil and Dyer, Chris and Hermann, Karl Moritz and Melis, Gábor and Grefenstette, Edward},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2018},
  doi = {10.1162/tacl\_a\_00023},
  url = {https://doi.org/10.1162/tacl_a_00023}
}
```

- [← previous The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation](https://md.research.elamur.ai/free/en/papers/klie-2018-inception-platform-machine-assisted-knowledge/)
- [next → The Natural Language Decathlon: Multitask Learning as Question Answering](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/4/)

## References

33 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ondrej Bajgar, Rudolf Kadlec, and Jan Kleindienst. 2016. Embracing data abundance: Booktest dataset for reading comprehension. CoRR, arXiv:1610.00956. — [arXiv:1610.00956](https://arxiv.org/abs/1610.00956) [find](https://scholar.google.com/scholar?q=Ondrej+Bajgar%2C+Rudolf+Kadlec%2C+and+Jan+Kleindienst%2E+2016%2E+Embracing+data+abundance%3A+Booktest+dataset+for+reading+comprehension%2E+CoRR%2C+arXiv%3A1610%2E00956%2E+%E2%80%94+%5BarXiv%3A1610%2E00956%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1610%2E0)
2. David Bamman, Brendan O’Connor, and Noah A. Smith. 2014a. Learning latent personas of film characters. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, page 352. [find](https://scholar.google.com/scholar?q=David+Bamman%2C+Brendan+O%E2%80%99Connor%2C+and+Noah+A%2E+Smith%2E+2014a%2E+Learning+latent+personas+of+film+characters%2E+In+Proceedings+of+the+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2C+page+352%2E) [our card](https://md.research.elamur.ai/free/en/papers/bamman-2013-learning-latent-personas-film-characters/)
3. David Bamman, Ted Underwood, and Noah A. Smith. 2014b. A Bayesian mixed effects model of literary character. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 370–379. [find](https://scholar.google.com/scholar?q=David+Bamman%2C+Ted+Underwood%2C+and+Noah+A%2E+Smith%2E+2014b%2E+A+Bayesian+mixed+effects+model+of+literary+character%2E+In+Proceedings+of+the+52nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+) [our card](https://md.research.elamur.ai/free/en/papers/bamman-2014-bayesian-mixed-effects-model-literary/)
4. John B. Black and Robert Wilensky. 1979. An evaluation of story grammars. Cognitive Science, 3(3):213–229. [find](https://scholar.google.com/scholar?q=John+B%2E+Black+and+Robert+Wilensky%2E+1979%2E+An+evaluation+of+story+grammars%2E+Cognitive+Science%2C+3%283%29%3A213%E2%80%93229%2E)
5. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan):993–1022. [find](https://scholar.google.com/scholar?q=David+M%2E+Blei%2C+Andrew+Y%2E+Ng%2C+and+Michael+I%2E+Jordan%2E+2003%2E+Latent+dirichlet+allocation%2E+Journal+of+Machine+Learning+Research%2C+3%28Jan%29%3A993%E2%80%931022%2E) [our card](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/)
6. Nathanael Chambers and Dan Jurafsky. 2009. Unsupervised learning of narrative schemas and their participants. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2, ACL… [find](https://scholar.google.com/scholar?q=Nathanael+Chambers+and+Dan+Jurafsky%2E+2009%2E+Unsupervised+learning+of+narrative+schemas+and+their+participants%2E+In+Proceedings+of+the+Joint+Conference+of+the+47th+Annual+Meeting+of+the+ACL+and+the+4th+I)
7. Snigdha Chaturvedi, Mohit Iyyer, and Hal Daumé III. 2017. Unsupervised learning of evolving relationships between literary characters. In Association for the Advancement of Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Snigdha+Chaturvedi%2C+Mohit+Iyyer%2C+and+Hal+Daume%CC%81+III%2E+2017%2E+Unsupervised+learning+of+evolving+relationships+between+literary+characters%2E+In+Association+for+the+Advancement+of+Artificial+Intelligence%2E)
8. Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016. A thorough examination of the CNN/Daily Mail reading comprehension task. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Danqi+Chen%2C+Jason+Bolton%2C+and+Christopher+D%2E+Manning%2E+2016%2E+A+thorough+examination+of+the+CNN%2FDaily+Mail+reading+comprehension+task%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Co) [our card](https://md.research.elamur.ai/free/en/papers/chen-2016-thorough-examination-cnn-daily-mail/)
9. Michael Denkowski and Alon Lavie. 2011. Meteor 1.3: Automatic Metric for Reliable Optimization and Evaluation of Machine Translation Systems. In Proceedings of the EMNLP 2011 Workshop on Statistical Machine Translation, pages 85–91. [find](https://scholar.google.com/scholar?q=Michael+Denkowski+and+Alon+Lavie%2E+2011%2E+Meteor+1%2E3%3A+Automatic+Metric+for+Reliable+Optimization+and+Evaluation+of+Machine+Translation+Systems%2E+In+Proceedings+of+the+EMNLP+2011+Workshop+on+Statistical+M) [our card](https://md.research.elamur.ai/free/en/papers/denkowski-2011-meteor-1-3-automatic-metric/)
10. Matthew Dunn, Levent Sagun, Mike Higgins, Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017. SearchQA: A new Q&A dataset augmented with context from a search engine. arXiv:1704.05179v2. [find](https://scholar.google.com/scholar?q=Matthew+Dunn%2C+Levent+Sagun%2C+Mike+Higgins%2C+Ugur+Guney%2C+Volkan+Cirik%2C+and+Kyunghyun+Cho%2E+2017%2E+SearchQA%3A+A+new+Q%26A+dataset+augmented+with+context+from+a+search+engine%2E+arXiv%3A1704%2E05179v2%2E) [our card](https://md.research.elamur.ai/free/en/papers/dunn-2017-searchqa-new-q-dataset-augmented/)
11. Lea Frermann and György Szarvas. 2017. Inducing semantic micro-clusters from deep multi-view representations of novels. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1874–1884. [find](https://scholar.google.com/scholar?q=Lea+Frermann+and+Gyo%CC%88rgy+Szarvas%2E+2017%2E+Inducing+semantic+micro%2Dclusters+from+deep+multi%2Dview+representations+of+novels%2E+In+Proceedings+of+the+2017+Conference+on+Empirical+Methods+in+Natural+Language+)
12. Philip John Gorinski and Mirella Lapata. 2015. Movie script summarization as graph-based scene extraction. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1066–1076, May–June. [find](https://scholar.google.com/scholar?q=Philip+John+Gorinski+and+Mirella+Lapata%2E+2015%2E+Movie+script+summarization+as+graph%2Dbased+scene+extraction%2E+In+Proceedings+of+the+2015+Conference+of+the+North+American+Chapter+of+the+Association+for+Co)
13. Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. In Advances in Neural Information Processing Systems, pages 1693–1701. [find](https://scholar.google.com/scholar?q=Karl+Moritz+Hermann%2C+Toma%CC%81s%CC%8C+Koc%CC%8Cisky%CC%81%2C+Edward+Grefenstette%2C+Lasse+Espeholt%2C+Will+Kay%2C+Mustafa+Suleyman%2C+and+Phil+Blunsom%2E+2015%2E+Teaching+machines+to+read+and+comprehend%2E+In+Advances+in+Neural+Informa) [our card](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/)
14. Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, and David Berthelot. 2016. WikiReading: A novel large-scale language understanding task over wikipedia. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguis… [find](https://scholar.google.com/scholar?q=Daniel+Hewlett%2C+Alexandre+Lacoste%2C+Llion+Jones%2C+Illia+Polosukhin%2C+Andrew+Fandrianto%2C+Jay+Han%2C+Matthew+Kelcey%2C+and+David+Berthelot%2E+2016%2E+WikiReading%3A+A+novel+large%2Dscale+language+understanding+task+ov)
15. Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016. The goldilocks principle: Reading children’s books with explicit memory representations. In Proceedings of ICLR. [find](https://scholar.google.com/scholar?q=Felix+Hill%2C+Antoine+Bordes%2C+Sumit+Chopra%2C+and+Jason+Weston%2E+2016%2E+The+goldilocks+principle%3A+Reading+children%E2%80%99s+books+with+explicit+memory+representations%2E+In+Proceedings+of+ICLR%2E) [our card](https://md.research.elamur.ai/free/en/papers/hill-2015-goldilocks-principle-reading-children-s/)
16. Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daumé III. 2016. Feuding families and former friends: Unsupervised learning for dynamic fictional relationships. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguis… [find](https://scholar.google.com/scholar?q=Mohit+Iyyer%2C+Anupam+Guha%2C+Snigdha+Chaturvedi%2C+Jordan+Boyd%2DGraber%2C+and+Hal+Daume%CC%81+III%2E+2016%2E+Feuding+families+and+former+friends%3A+Unsupervised+learning+for+dynamic+fictional+relationships%2E+In+Proceedin)
17. Rudolf Kadlec, Martin Schmid, Ondřej Bajgar, and Jan Kleindienst. 2016. Text understanding with the attention sum reader network. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 908–918. [find](https://scholar.google.com/scholar?q=Rudolf+Kadlec%2C+Martin+Schmid%2C+Ondr%CC%8Cej+Bajgar%2C+and+Jan+Kleindienst%2E+2016%2E+Text+understanding+with+the+attention+sum+reader+network%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Comp) [our card](https://md.research.elamur.ai/free/en/papers/kadlec-2016-text-understanding-attention-sum-reader/)
18. Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Proc. ACL Workshop on Text Summarization Branches Out. [find](https://scholar.google.com/scholar?q=Chin%2DYew+Lin%2E+2004%2E+ROUGE%3A+A+package+for+automatic+evaluation+of+summaries%2E+In+Proc%2E+ACL+Workshop+on+Text+Summarization+Branches+Out%2E)
19. Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. CoRR, arXiv:1611.09268. [find](https://scholar.google.com/scholar?q=Tri+Nguyen%2C+Mir+Rosenberg%2C+Xia+Song%2C+Jianfeng+Gao%2C+Saurabh+Tiwary%2C+Rangan+Majumder%2C+and+Li+Deng%2E+2016%2E+MS+MARCO%3A+A+human+generated+machine+reading+comprehension+dataset%2E+CoRR%2C+arXiv%3A1611%2E09268%2E) [our card](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/)
20. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pages 311–318. [find](https://scholar.google.com/scholar?q=Kishore+Papineni%2C+Salim+Roukos%2C+Todd+Ward%2C+and+Wei%2DJing+Zhu%2E+2002%2E+BLEU%3A+A+method+for+automatic+evaluation+of+machine+translation%2E+In+Proceedings+of+the+40th+Annual+Meeting+on+Association+for+Computat) [our card](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/)
21. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global vectors for word representation. In Processing of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543. [find](https://scholar.google.com/scholar?q=Jeffrey+Pennington%2C+Richard+Socher%2C+and+Christopher+D%2E+Manning%2E+2014%2E+GloVe%3A+Global+vectors+for+word+representation%2E+In+Processing+of+the+2014+Conference+on+Empirical+Methods+in+Natural+Language+Proce)
22. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of Empirical Methods in Natural Language Processing (EMNLP). [find](https://scholar.google.com/scholar?q=Pranav+Rajpurkar%2C+Jian+Zhang%2C+Konstantin+Lopyrev%2C+and+Percy+Liang%2E+2016%2E+SQuAD%3A+100%2C000%2B+questions+for+machine+comprehension+of+text%2E+In+Proceedings+of+Empirical+Methods+in+Natural+Language+Processing) [our card](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
23. Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013. MCTest: A challenge dataset for the open-domain machine comprehension of text. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 193– 203. [find](https://scholar.google.com/scholar?q=Matthew+Richardson%2C+Christopher+JC+Burges%2C+and+Erin+Renshaw%2E+2013%2E+MCTest%3A+A+challenge+dataset+for+the+open%2Ddomain+machine+comprehension+of+text%2E+In+Proceedings+of+the+2013+Conference+on+Empirical+Met) [our card](https://md.research.elamur.ai/free/en/papers/missing-2013-richardson-mctest-challenge-dataset-open-domain/)
24. Roger C. Schank and Robert P. Abelson. 1977. Scripts, Plans, Goals and Understanding: an Inquiry into Human Knowledge Structures. [find](https://scholar.google.com/scholar?q=Roger+C%2E+Schank+and+Robert+P%2E+Abelson%2E+1977%2E+Scripts%2C+Plans%2C+Goals+and+Understanding%3A+an+Inquiry+into+Human+Knowledge+Structures%2E)
25. L. Erlbaum, Hillsdale, NJ. Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional attention flow for machine comprehension. arXiv:1611.01603. [find](https://scholar.google.com/scholar?q=L%2E+Erlbaum%2C+Hillsdale%2C+NJ%2E+Minjoon+Seo%2C+Aniruddha+Kembhavi%2C+Ali+Farhadi%2C+and+Hannaneh+Hajishirzi%2E+2016%2E+Bidirectional+attention+flow+for+machine+comprehension%2E+arXiv%3A1611%2E01603%2E) [our card](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/)
26. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of Conference on the Advances in Neural Information Processing Systems, pages 3104–3112. [find](https://scholar.google.com/scholar?q=Ilya+Sutskever%2C+Oriol+Vinyals%2C+and+Quoc+V+Le%2E+2014%2E+Sequence+to+sequence+learning+with+neural+networks%2E+In+Proceedings+of+Conference+on+the+Advances+in+Neural+Information+Processing+Systems%2C+pages+310)
27. Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. 2016. MovieQA: Understanding stories in movies through question-answering. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4631– 4640. [find](https://scholar.google.com/scholar?q=Makarand+Tapaswi%2C+Yukun+Zhu%2C+Rainer+Stiefelhagen%2C+Antonio+Torralba%2C+Raquel+Urtasun%2C+and+Sanja+Fidler%2E+2016%2E+MovieQA%3A+Understanding+stories+in+movies+through+question%2Danswering%2E+In+IEEE+Conference+on+C)
28. Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016. NewsQA: A machine comprehension dataset. CoRR, arXiv:1611.09830. [find](https://scholar.google.com/scholar?q=Adam+Trischler%2C+Tong+Wang%2C+Xingdi+Yuan%2C+Justin+Harris%2C+Alessandro+Sordoni%2C+Philip+Bachman%2C+and+Kaheer+Suleman%2E+2016%2E+NewsQA%3A+A+machine+comprehension+dataset%2E+CoRR%2C+arXiv%3A1611%2E09830%2E) [our card](https://md.research.elamur.ai/free/en/papers/trischler-2017-newsqa-machine-comprehension-dataset/)
29. Shuohang Wang and Jing Jiang. 2016. Machine comprehension using match-LSTM and answer pointer. arXiv:1608.07905. [find](https://scholar.google.com/scholar?q=Shuohang+Wang+and+Jing+Jiang%2E+2016%2E+Machine+comprehension+using+match%2DLSTM+and+answer+pointer%2E+arXiv%3A1608%2E07905%2E) [our card](https://md.research.elamur.ai/free/en/papers/wang-2016-machine-comprehension-using-match-lstm/)
30. Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou. 2017. Gated self-matching networks for reading comprehension and question answering. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistcs, pages 189–198. [find](https://scholar.google.com/scholar?q=Wenhui+Wang%2C+Nan+Yang%2C+Furu+Wei%2C+Baobao+Chang%2C+and+Ming+Zhou%2E+2017%2E+Gated+self%2Dmatching+networks+for+reading+comprehension+and+question+answering%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Asso) [our card](https://md.research.elamur.ai/free/en/papers/wang-2017-gated-self-matching-networks-reading/)
31. Dirk Weissenborn, Georg Wiese, and Laura Seiffe. 2017. FastQA: A simple and efficient neural architecture for question answering. CoRR, arXiv:1703.04816v1. [find](https://scholar.google.com/scholar?q=Dirk+Weissenborn%2C+Georg+Wiese%2C+and+Laura+Seiffe%2E+2017%2E+FastQA%3A+A+simple+and+efficient+neural+architecture+for+question+answering%2E+CoRR%2C+arXiv%3A1703%2E04816v1%2E)
32. Jason Weston, Antoine Bordes, Sumit Chopra, and Tomas Mikolov. 2015. Towards AI-complete question answering: A set of prerequisite toy tasks. CoRR, arXiv:1502.05698. [find](https://scholar.google.com/scholar?q=Jason+Weston%2C+Antoine+Bordes%2C+Sumit+Chopra%2C+and+Tomas+Mikolov%2E+2015%2E+Towards+AI%2Dcomplete+question+answering%3A+A+set+of+prerequisite+toy+tasks%2E+CoRR%2C+arXiv%3A1502%2E05698%2E) [our card](https://md.research.elamur.ai/free/en/papers/weston-2015-towards-ai-complete-question-answering/)
33. R. Wilensky. 1978. Why John married Mary: Understanding stories involving recurring goals. Cognitive Science, 2(3):235–266. [find](https://scholar.google.com/scholar?q=R%2E+Wilensky%2E+1978%2E+Why+John+married+Mary%3A+Understanding+stories+involving+recurring+goals%2E+Cognitive+Science%2C+2%283%29%3A235%E2%80%93266%2E)