---
title: "Reading Wikipedia to Answer Open-Domain — Chen 2017"
description: "The paper tackles open-domain factoid question answering where Wikipedia is the only knowledge source, so each answer must be a text span in one of its…"
authors: ["Danqi Chen", "Adam Fisch", "Jason Weston", "Antoine Bordes"]
year: 2017
venue: "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P17-1171/"
doi: "10.18653/v1/p17-1171"
arxiv: "1704.00051"
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/chen-2017-reading-wikipedia-answer-open-domain/"
---
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# Reading Wikipedia to Answer Open-Domain Questions

**Authors:** [Danqi Chen](https://md.research.elamur.ai/free/en/authors/danqi-chen/), [Adam Fisch](https://md.research.elamur.ai/free/en/authors/adam-fisch/), [Jason Weston](https://md.research.elamur.ai/free/en/authors/jason-weston/), [Antoine Bordes](https://md.research.elamur.ai/free/en/authors/antoine-bordes/) · **Institutions:** [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) · [Meta](https://md.research.elamur.ai/free/en/companies/meta/) · **Year:** 2017 · **Venue:** Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P17-1171/) · [PDF](https://aclanthology.org/P17-1171.pdf) · [DOI 10.18653/v1/p17-1171](https://doi.org/10.18653/v1/p17-1171) · [arXiv 1704.00051](https://arxiv.org/abs/1704.00051) · [OpenAlex](https://openalex.org/W2962985038)

**Citations:** 1499 citations (OpenAlex, 2026-10-14) · [751](https://md.research.elamur.ai/free/en/papers/chen-2017-reading-wikipedia-answer-open-domain/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper tackles open-domain factoid question answering where Wikipedia is the only knowledge source, so each answer must be a text span in one of its more than 5 million articles. The authors call this setting machine reading at scale (MRS): a system has to find the right documents first, then read them closely enough to pick out the exact answer. Their system, DrQA, has two parts. Document Retriever is a search module with no learning that ranks articles against the question using TF-IDF over hashed unigram and bigram features. Document Reader is a multi-layer bidirectional LSTM that predicts answer spans in paragraphs. Its inputs are GloVe embeddings, token features, exact-match signals and an attention-based soft alignment with the question. On its own, the reader is competitive with the best single models on SQuAD. For the full system, the authors build distantly supervised training sets from CuratedTREC, WebQuestions and WikiMovies and train one model on all of them together. This beats SQuAD-only training on all four benchmarks. The study shows that reading comprehension alone does not solve open-domain QA and offers a simple retriever-plus-reader design that works on any document collection.

## Key points
- Document Retriever hashes bigrams into 2^24 bins with an unsigned murmur3 hash and returns the top 5 articles per question. It beats the built-in ElasticSearch-based Wikipedia Search API and finds an article containing the answer for 77.8% of SQuAD questions.
- Document Reader scores 69.5 EM / 78.8 F1 on the SQuAD dev set and 70.0 EM / 79.0 F1 on test. If both the aligned-embedding and exact-match features are removed, dev F1 falls to 59.4.
- On full Wikipedia, the Multitask (DS) model reaches top-1 exact match of 29.8 on SQuAD, 25.4 on CuratedTREC, 20.7 on WebQuestions and 36.5 on WikiMovies.
- On SQuAD, moving from a given paragraph to all of Wikipedia cuts accuracy sharply (69.5 → 27.1 for the SQuAD-only model). Many errors come from sentences that are on topic but don't contain the answer, and from SQuAD questions that are unclear without their source paragraph.
- YodaQA uses several sources (Freebase, DBpedia and others) and still stays ahead: 31.3 vs 25.4 on CuratedTREC and 39.8 vs 20.7 on WebQuestions.

*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/)

## Cite

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

```bibtex
@inproceedings{chen2017reading,
  title = {{Reading Wikipedia to Answer Open-Domain Questions}},
  author = {Chen, Danqi and Fisch, Adam and Weston, Jason and Bordes, Antoine},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2017},
  doi = {10.18653/v1/p17-1171},
  eprint = {1704.00051},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/p17-1171}
}
```

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## References

34 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Eric Brill, Susan Dumais, and Michele Banko. 2002. An analysis of the AskMSR question-answering system. In Empirical Methods in Natural Language Processing (EMNLP). pages 257–264. [find](https://scholar.google.com/scholar?q=Eric+Brill%2C+Susan+Dumais%2C+and+Michele+Banko%2E+2002%2E+An+analysis+of+the+AskMSR+question%2Danswering+system%2E+In+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2E+pages+257%E2%80%93264%2E)
2. David Ahn, Valentin Jijkoun, Gilad Mishne, Karin Mller, Maarten de Rijke, and Stefan Schlobach. 2004. Using wikipedia at the trec qa track. In Proceedings of TREC 2004. [find](https://scholar.google.com/scholar?q=David+Ahn%2C+Valentin+Jijkoun%2C+Gilad+Mishne%2C+Karin+Mller%2C+Maarten+de+Rijke%2C+and+Stefan+Schlobach%2E+2004%2E+Using+wikipedia+at+the+trec+qa+track%2E+In+Proceedings+of+TREC+2004%2E)
3. Davide Buscaldi and Paolo Rosso. 2006. Mining knowledge from Wikipedia for the question answering task. In International Conference on Language Resources and Evaluation (LREC). pages 727–730. [find](https://scholar.google.com/scholar?q=Davide+Buscaldi+and+Paolo+Rosso%2E+2006%2E+Mining+knowledge+from+Wikipedia+for+the+question+answering+task%2E+In+International+Conference+on+Language+Resources+and+Evaluation+%28LREC%29%2E+pages+727%E2%80%93730%2E)
4. Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007. Dbpedia: A nucleus for a web of open data. In The semantic web, Springer, pages 722–735. [find](https://scholar.google.com/scholar?q=So%CC%88ren+Auer%2C+Christian+Bizer%2C+Georgi+Kobilarov%2C+Jens+Lehmann%2C+Richard+Cyganiak%2C+and+Zachary+Ives%2E+2007%2E+Dbpedia%3A+A+nucleus+for+a+web+of+open+data%2E+In+The+semantic+web%2C+Springer%2C+pages+722%E2%80%93735%2E)
5. Rich Caruana. 1998. Multitask learning. In Learning to learn, Springer, pages 95–133. [find](https://scholar.google.com/scholar?q=Rich+Caruana%2E+1998%2E+Multitask+learning%2E+In+Learning+to+learn%2C+Springer%2C+pages+95%E2%80%93133%2E)
6. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Dzmitry+Bahdanau%2C+Kyunghyun+Cho%2C+and+Yoshua+Bengio%2E+2015%2E+Neural+machine+translation+by+jointly+learning+to+align+and+translate%2E+In+International+Conference+on+Learning+Representations+%28ICLR%29%2E) [our card](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
7. Petr Baudiš. 2015. YodaQA: a modular question answering system pipeline. In POSTER 2015-19th International Student Conference on Electrical Engineering. pages 1156–1165. [find](https://scholar.google.com/scholar?q=Petr+Baudis%CC%8C%2E+2015%2E+YodaQA%3A+a+modular+question+answering+system+pipeline%2E+In+POSTER+2015%2D19th+International+Student+Conference+on+Electrical+Engineering%2E+pages+1156%E2%80%931165%2E)
8. Petr Baudiš and Jan Šedivỳ. 2015. Modeling of the question answering task in the YodaQA system. In International Conference of the Cross-Language Evaluation Forum for European Languages. Springer, pages 222–228. [find](https://scholar.google.com/scholar?q=Petr+Baudis%CC%8C+and+Jan+S%CC%8Cedivy%CC%80%2E+2015%2E+Modeling+of+the+question+answering+task+in+the+YodaQA+system%2E+In+International+Conference+of+the+Cross%2DLanguage+Evaluation+Forum+for+European+Languages%2E+Springer%2C+)
9. Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. Semantic parsing on freebase Danqi Chen, Jason Bolton, and Christopher D Manning. 2016. A thorough examination of the CNN/Daily Mail reading comprehension task. In Association for Computational Linguistics (ACL). [find](https://scholar.google.com/scholar?q=Jonathan+Berant%2C+Andrew+Chou%2C+Roy+Frostig%2C+and+Percy+Liang%2E+2013%2E+Semantic+parsing+on+freebase+Danqi+Chen%2C+Jason+Bolton%2C+and+Christopher+D+Manning%2E+2016%2E+A+thorough+examination+of+the+CNN%2FDaily+Mail+r)
10. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: deep neural networks with multitask learning. In International Conference on Machine Learning (ICML). [find](https://scholar.google.com/scholar?q=Ronan+Collobert+and+Jason+Weston%2E+2008%2E+A+unified+architecture+for+natural+language+processing%3A+deep+neural+networks+with+multitask+learning%2E+In+International+Conference+on+Machine+Learning+%28ICML%29%2E)
11. Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. 2014. Open question answering over curated and extracted knowledge bases. In ACM SIGKDD international conference on Knowledge discovery and data mining. pages 1156–1165. [find](https://scholar.google.com/scholar?q=Anthony+Fader%2C+Luke+Zettlemoyer%2C+and+Oren+Etzioni%2E+2014%2E+Open+question+answering+over+curated+and+extracted+knowledge+bases%2E+In+ACM+SIGKDD+international+conference+on+Knowledge+discovery+and+data+mini)
12. David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A Kalyanpur, Adam Lally, J William Murdock, Eric Nyberg, John Prager, et al. 2010. Building Watson: An overview of the DeepQA project. AI magazine 31(3):59–79. [find](https://scholar.google.com/scholar?q=David+Ferrucci%2C+Eric+Brown%2C+Jennifer+Chu%2DCarroll%2C+James+Fan%2C+David+Gondek%2C+Aditya+A+Kalyanpur%2C+Adam+Lally%2C+J+William+Murdock%2C+Eric+Nyberg%2C+John+Prager%2C+et+al%2E+2010%2E+Building+Watson%3A+An+overview+of+the)
13. Clinton Gormley and Zachary Tong. 2015. Elasticsearch: The Definitive Guide. ” O’Reilly Media, Inc.”. [find](https://scholar.google.com/scholar?q=Clinton+Gormley+and+Zachary+Tong%2E+2015%2E+Elasticsearch%3A+The+Definitive+Guide%2E+%E2%80%9D+O%E2%80%99Reilly+Media%2C+Inc%2E%E2%80%9D%2E)
14. Alex Graves, Greg Wayne, and Ivo Danihelka. 2014. Neural turing machines. arXiv preprint arXiv:1410.5401 . [find](https://scholar.google.com/scholar?q=Alex+Graves%2C+Greg+Wayne%2C+and+Ivo+Danihelka%2E+2014%2E+Neural+turing+machines%2E+arXiv+preprint+arXiv%3A1410%2E5401+%2E)
15. 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 (NIPS). [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/)
16. 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 Association for Computational Linguistics (ACL). pages 1535–1545. [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)
17. Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL/IJCNLP). pages 1003–1011. [find](https://scholar.google.com/scholar?q=Mike+Mintz%2C+Steven+Bills%2C+Rion+Snow%2C+and+Daniel+Jurafsky%2E+2009%2E+Distant+supervision+for+relation+extraction+without+labeled+data%2E+In+Association+for+Computational+Linguistics+and+International+Joint+C)
18. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In 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+Manning%2E+2014%2E+Glove%3A+Global+vectors+for+word+representation%2E+In+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2E+pages+1532%E2%80%931543%2E) [our card](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/)
19. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In 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+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29%2E) [our card](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
20. Pum-Mo Ryu, Myung-Gil Jang, and Hyun-Ki Kim. 2014. Open domain question answering using Wikipedia-based knowledge model. Information Processing & Management 50(5):683–692. [find](https://scholar.google.com/scholar?q=Pum%2DMo+Ryu%2C+Myung%2DGil+Jang%2C+and+Hyun%2DKi+Kim%2E+2014%2E+Open+domain+question+answering+using+Wikipedia%2Dbased+knowledge+model%2E+Information+Processing+%26+Management+50%285%29%3A683%E2%80%93692%2E)
21. Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016. The Goldilocks Principle: Reading children’s books with explicit memory representations. In International Conference on Learning Representations (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+International+Conference+on+Learning+Repre) [our card](https://md.research.elamur.ai/free/en/papers/hill-2015-goldilocks-principle-reading-children-s/)
22. Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603 . [find](https://scholar.google.com/scholar?q=Minjoon+Seo%2C+Aniruddha+Kembhavi%2C+Ali+Farhadi%2C+and+Hannaneh+Hajishirzi%2E+2016%2E+Bidirectional+attention+flow+for+machine+comprehension%2E+arXiv+preprint+arXiv%3A1611%2E01603+%2E) [our card](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/)
23. Minyoung Huh, Pulkit Agrawal, and Alexei A Efros. 2016. What makes ImageNet good for transfer learning? arXiv preprint arXiv:1608.08614 . — [arXiv:1608.08614](https://arxiv.org/abs/1608.08614) [find](https://scholar.google.com/scholar?q=Minyoung+Huh%2C+Pulkit+Agrawal%2C+and+Alexei+A+Efros%2E+2016%2E+What+makes+ImageNet+good+for+transfer+learning%3F+arXiv+preprint+arXiv%3A1608%2E08614+%2E+%E2%80%94+%5BarXiv%3A1608%2E08614%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1608%2E08614%29)
24. Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, and Ming-Wei Chang. 2015. Open domain question answering via semantic enrichment. In Proceedings of the 24th International Conference on World Wide Web. ACM, pages 1045–1055. [find](https://scholar.google.com/scholar?q=Huan+Sun%2C+Hao+Ma%2C+Wen%2Dtau+Yih%2C+Chen%2DTse+Tsai%2C+Jingjing+Liu%2C+and+Ming%2DWei+Chang%2E+2015%2E+Open+domain+question+answering+via+semantic+enrichment%2E+In+Proceedings+of+the+24th+International+Conference+on+Wor)
25. Mohit Iyyer, Jordan L Boyd-Graber, Leonardo Max Batista Claudino, Richard Socher, and Hal Daumé III. 2014. A neural network for factoid question answering over paragraphs. In Empirical Methods in Natural Language Processing (EMNLP). pages 633–644. [find](https://scholar.google.com/scholar?q=Mohit+Iyyer%2C+Jordan+L+Boyd%2DGraber%2C+Leonardo+Max+Batista+Claudino%2C+Richard+Socher%2C+and+Hal+Daume%CC%81+III%2E+2014%2E+A+neural+network+for+factoid+question+answering+over+paragraphs%2E+In+Empirical+Methods+in+Nat)
26. Zhiguo Wang, Haitao Mi, Wael Hamza, and Radu Florian. 2016. Multi-perspective context matching for machine comprehension. arXiv preprint arXiv:1612.04211 . — [arXiv:1612.04211](https://arxiv.org/abs/1612.04211) [find](https://scholar.google.com/scholar?q=Zhiguo+Wang%2C+Haitao+Mi%2C+Wael+Hamza%2C+and+Radu+Florian%2E+2016%2E+Multi%2Dperspective+context+matching+for+machine+comprehension%2E+arXiv+preprint+arXiv%3A1612%2E04211+%2E+%E2%80%94+%5BarXiv%3A1612%2E04211%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1)
27. Rudolf Kadlec, Ondrej Bajgar, and Jan Kleindienst. 2016. From particular to general: A preliminary case study of transfer learning in reading comprehension. Machine Intelligence Workshop, NIPS . [find](https://scholar.google.com/scholar?q=Rudolf+Kadlec%2C+Ondrej+Bajgar%2C+and+Jan+Kleindienst%2E+2016%2E+From+particular+to+general%3A+A+preliminary+case+study+of+transfer+learning+in+reading+comprehension%2E+Machine+Intelligence+Workshop%2C+NIPS+%2E)
28. Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg. 2009. Feature hashing for large scale multitask learning. In International Conference on Machine Learning (ICML). pages 1113–1120. [find](https://scholar.google.com/scholar?q=Kilian+Weinberger%2C+Anirban+Dasgupta%2C+John+Langford%2C+Alex+Smola%2C+and+Josh+Attenberg%2E+2009%2E+Feature+hashing+for+large+scale+multitask+learning%2E+In+International+Conference+on+Machine+Learning+%28ICML%29%2E+pa)
29. Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . [find](https://scholar.google.com/scholar?q=Diederik+Kingma+and+Jimmy+Ba%2E+2014%2E+Adam%3A+A+method+for+stochastic+optimization%2E+arXiv+preprint+arXiv%3A1412%2E6980+%2E)
30. Jason Weston, Sumit Chopra, and Antoine Bordes. 2015. Memory networks. In International Conference on Learning Representations (ICLR). [find](https://scholar.google.com/scholar?q=Jason+Weston%2C+Sumit+Chopra%2C+and+Antoine+Bordes%2E+2015%2E+Memory+networks%2E+In+International+Conference+on+Learning+Representations+%28ICLR%29%2E)
31. Kenton Lee, Tom Kwiatkowski, Ankur Parikh, and Dipanjan Das. 2016. Learning recurrent span representations for extractive question answering. arXiv preprint arXiv:1611.01436 . — [arXiv:1611.01436](https://arxiv.org/abs/1611.01436) [find](https://scholar.google.com/scholar?q=Kenton+Lee%2C+Tom+Kwiatkowski%2C+Ankur+Parikh%2C+and+Dipanjan+Das%2E+2016%2E+Learning+recurrent+span+representations+for+extractive+question+answering%2E+arXiv+preprint+arXiv%3A1611%2E01436+%2E+%E2%80%94+%5BarXiv%3A1611%2E01436%5D%28htt)
32. Caiming Xiong, Victor Zhong, and Richard Socher. 2016. Dynamic coattention networks for question answering. arXiv preprint arXiv:1611.01604 . [find](https://scholar.google.com/scholar?q=Caiming+Xiong%2C+Victor+Zhong%2C+and+Richard+Socher%2E+2016%2E+Dynamic+coattention+networks+for+question+answering%2E+arXiv+preprint+arXiv%3A1611%2E01604+%2E) [our card](https://md.research.elamur.ai/free/en/papers/xiong-2016-dynamic-coattention-networks-question-answering/)
33. Christopher D Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J Bethard, and David Mc-Closky. 2014. The stanford corenlp natural language processing toolkit. In Association for Computational Linguistics (ACL). pages 55–60. [find](https://scholar.google.com/scholar?q=Christopher+D+Manning%2C+Mihai+Surdeanu%2C+John+Bauer%2C+Jenny+Finkel%2C+Steven+J+Bethard%2C+and+David+Mc%2DClosky%2E+2014%2E+The+stanford+corenlp+natural+language+processing+toolkit%2E+In+Association+for+Computational) [our card](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/)
34. Alexander H. Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston. 2016. Key-value memory networks for directly reading documents. In Empirical Methods in Natural Language Processing (EMNLP). pages 1400– 1409. [find](https://scholar.google.com/scholar?q=Alexander+H%2E+Miller%2C+Adam+Fisch%2C+Jesse+Dodge%2C+Amir%2DHossein+Karimi%2C+Antoine+Bordes%2C+and+Jason+Weston%2E+2016%2E+Key%2Dvalue+memory+networks+for+directly+reading+documents%2E+In+Empirical+Methods+in+Natural+Lan)