---
title: "What Makes Reading Comprehension Questions Easier? (2018)"
description: "Machine reading comprehension (MRC) benchmarks are meant to test language understanding, but… 43 references, cited by 73 papers here."
authors: ["Saku Sugawara", "Kentaro Inui", "Satoshi Sekine", "Akiko Aizawa"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1453/"
doi: "10.18653/v1/d18-1453"
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/sugawara-2018-what-makes-reading-comprehension-questions/"
---
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# What Makes Reading Comprehension Questions Easier?

**Authors:** [Saku Sugawara](https://md.research.elamur.ai/free/en/authors/saku-sugawara/), [Kentaro Inui](https://md.research.elamur.ai/free/en/authors/kentaro-inui/), [Satoshi Sekine](https://md.research.elamur.ai/free/en/authors/satoshi-sekine/) and [Akiko Aizawa](https://md.research.elamur.ai/free/en/authors/akiko-aizawa/) · **Year:** 2018 · **Venue:** [Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2018](https://md.research.elamur.ai/free/en/venues/emnlp/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1453/) · [PDF](https://aclanthology.org/D18-1453.pdf) · [DOI 10.18653/v1/d18-1453](https://doi.org/10.18653/v1/d18-1453) · [OpenAlex](https://openalex.org/W2889453388)

**Citations:** 105 citations (OpenAlex, 2026-10-14) · [73](https://md.research.elamur.ai/free/en/papers/sugawara-2018-what-makes-reading-comprehension-questions/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Machine reading comprehension (MRC) benchmarks are meant to test language understanding, but many of their questions may be solvable through shallow shortcuts. This paper measures how common such shortcuts are across 12 recent MRC datasets in three formats: span extraction, free-form description, and multiple choice. The authors use two simple filters to divide each dataset into easy and hard portions:
- **Truncated questions:** whether a model can still answer when it sees only the first k tokens of the question, which captures entity-type cues.
- **Most similar sentence:** whether the gold answer lies in the context sentence with the highest word overlap with the question, which captures attention-style matching.

They then compare baseline scores on each portion. They also hand-annotate sampled questions for validity and for the reasoning they demand. Baseline performance falls sharply on the hard portions. Hard questions more often call for knowledge inference and for combining several sentences. Multiple-choice data covers a wider variety of reasoning than the other two formats. The authors conclude that reported MRC progress may be inflated and that dataset creators should check for such heuristics.

## Key points
- **Datasets:** SQuAD v1.1, AddSent, NewsQA, TriviaQA, QAngaroo (WikiHop), MS MARCO v2, NarrativeQA, MCTest, RACE, MCScript, ARC-E and ARC-C.
- **Baselines and metrics:**
  - BiDAF for the extraction and description sets, scored with EM/F1 and Rouge-L respectively.
  - Gated-Attention Reader for the multiple-choice sets, scored with accuracy.
- **Truncation filter:** k was set to 1, 2 or 4 tokens, well below the average question length of 12.2 tokens.
- **Baseline reproduction:** the authors' BiDAF scored 67.9/77.2 EM/F1 on SQuAD dev. Their reproductions fell short of the official results on TriviaQA, MCTest, RACE and ARC-E.
- **Released resources:** scripts, subsets and annotations are available at github.com/Alab-NII/mrc-heuristics.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Saku Sugawara, Kentaro Inui, Satoshi Sekine and Akiko Aizawa. 2018. What Makes Reading Comprehension Questions Easier?. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{sugawara2018what,
  title = {{What Makes Reading Comprehension Questions Easier?}},
  author = {Saku Sugawara, Kentaro Inui, Satoshi Sekine and Akiko Aizawa},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1453},
  url = {https://doi.org/10.18653/v1/d18-1453}
}
```

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## References

43 references, 22 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Aishwarya Agrawal, Dhruv Batra, and Devi Parikh. 2016. [Analyzing the behavior of visual question answering models](https://md.research.elamur.ai/free/en/papers/agrawal-2016-analyzing-behavior-visual-question-answering/). In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1955–1960, Austin, Texas. Association for Computational Linguistics.
2. Michael Boratko, Harshit Padigela, Divyendra Mikkilineni, Pritish Yuvraj, Rajarshi Das, Andrew McCallum, Maria Chang, Achille Fokoue-Nkoutche, Pavan Kapanipathi, Nicholas Mattei, Ryan Musa, Kartik Talamadupula, and Michael Witbrock. 2018. A systematic classification of knowledge, reasoning, and cont… [find](https://scholar.google.com/scholar?q=Michael+Boratko%2C+Harshit+Padigela%2C+Divyendra+Mikkilineni%2C+Pritish+Yuvraj%2C+Rajarshi+Das%2C+Andrew+McCallum%2C+Maria+Chang%2C+Achille+Fokoue%2DNkoutche%2C+Pavan+Kapanipathi%2C+Nicholas+Mattei%2C+Ryan+Musa%2C+Kartik+Tal)
3. Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016. [A thorough examination of the CNN/Daily Mail reading comprehension task](https://md.research.elamur.ai/free/en/papers/chen-2016-thorough-examination-cnn-daily-mail/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 2358– 2367. Association for Computational Linguistics.
4. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. Think you have solved question answering? Try ARC, the AI2 reasoning challenge. CoRR, abs/1803.05457. [find](https://scholar.google.com/scholar?q=Peter+Clark%2C+Isaac+Cowhey%2C+Oren+Etzioni%2C+Tushar+Khot%2C+Ashish+Sabharwal%2C+Carissa+Schoenick%2C+and+Oyvind+Tafjord%2E+2018%2E+Think+you+have+solved+question+answering%3F+Try+ARC%2C+the+AI2+reasoning+challenge%2E+CoR)
5. Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006. The PASCAL recognising textual entailment challenge. In Machine learning challenges. evaluating predictive uncertainty, visual object classification, and recognising tectual entailment, pages 177– 190. Springer. [find](https://scholar.google.com/scholar?q=Ido+Dagan%2C+Oren+Glickman%2C+and+Bernardo+Magnini%2E+2006%2E+The+PASCAL+recognising+textual+entailment+challenge%2E+In+Machine+learning+challenges%2E+evaluating+predictive+uncertainty%2C+visual+object+classificati)
6. Bhavana Dalvi, Lifu Huang, Niket Tandon, Wen-tau Yih, and Peter Clark. 2018. Tracking state changes in procedural text: a challenge dataset and models for process paragraph comprehension. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguis… [find](https://scholar.google.com/scholar?q=Bhavana+Dalvi%2C+Lifu+Huang%2C+Niket+Tandon%2C+Wen%2Dtau+Yih%2C+and+Peter+Clark%2E+2018%2E+Tracking+state+changes+in+procedural+text%3A+a+challenge+dataset+and+models+for+process+paragraph+comprehension%2E+In+Proceedin)
7. Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William Cohen, and Ruslan Salakhutdinov. 2017. Gated-attention readers for text comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1832–1846. Association for Computational… [find](https://scholar.google.com/scholar?q=Bhuwan+Dhingra%2C+Hanxiao+Liu%2C+Zhilin+Yang%2C+William+Cohen%2C+and+Ruslan+Salakhutdinov%2E+2017%2E+Gated%2Dattention+readers+for+text+comprehension%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+fo)
8. Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018. Annotation artifacts in natural language inference data. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno… [find](https://scholar.google.com/scholar?q=Suchin+Gururangan%2C+Swabha+Swayamdipta%2C+Omer+Levy%2C+Roy+Schwartz%2C+Samuel+Bowman%2C+and+Noah+A%2E+Smith%2E+2018%2E+Annotation+artifacts+in+natural+language+inference+data%2E+In+Proceedings+of+the+2018+Conference+o)
9. Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. [Teaching machines to read and comprehend](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/). In Advances in Neural Information Processing Systems (NIPS), pages 1693–1701.
10. José Hernández-Orallo. 2017. Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement. Artificial Intelligence Review, 48(3):397–447. [find](https://scholar.google.com/scholar?q=Jose%CC%81+Herna%CC%81ndez%2DOrallo%2E+2017%2E+Evaluation+in+artificial+intelligence%3A+from+task%2Doriented+to+ability%2Doriented+measurement%2E+Artificial+Intelligence+Review%2C+48%283%29%3A397%E2%80%93447%2E)
11. 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)
12. Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016. [The goldilocks principle: Reading children’s books with explicit memory representations](https://md.research.elamur.ai/free/en/papers/hill-2015-goldilocks-principle-reading-children-s/). In the International Conference on Learning Representations.
13. Lynette Hirschman, Marc Light, Eric Breck, and John D. Burger. 1999. Deep read: A reading comprehension system. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pages 325–332. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Lynette+Hirschman%2C+Marc+Light%2C+Eric+Breck%2C+and+John+D%2E+Burger%2E+1999%2E+Deep+read%3A+A+reading+comprehension+system%2E+In+Proceedings+of+the+37th+Annual+Meeting+of+the+Association+for+Computational+Linguisti)
14. Robin Jia and Percy Liang. 2017. [Adversarial examples for evaluating reading comprehension systems](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/). In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2011–2021, Copenhagen, Denmark. Association for Computational Linguistics.
15. Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. [TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/). In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1601– 1611. Associat…
16. Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018. [Looking beyond the surface: A challenge set for reading comprehension over multiple sentences](https://md.research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/). In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:…
17. Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018. [The NarrativeQA reading comprehension challenge](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/). Transactions of the Association for Computational Linguistics, 6:317– 328.
18. Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017. [RACE: Large-scale reading comprehension dataset from examinations](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/). In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 796–805, Copenhagen, Denmark. Association for Computational Ling…
19. Hector J. Levesque. 2014. On our best behaviour. Artificial Intelligence, 212:27 – 35. [find](https://scholar.google.com/scholar?q=Hector+J%2E+Levesque%2E+2014%2E+On+our+best+behaviour%2E+Artificial+Intelligence%2C+212%3A27+%E2%80%93+35%2E)
20. Chin-Yew Lin. 2004. [ROUGE: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.
21. Peter LoBue and Alexander Yates. 2011. [Types of common-sense knowledge needed for recognizing textual entailment](https://md.research.elamur.ai/free/en/papers/lobue-2011-types-common-sense-knowledge-needed/). In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 329–334, Portland, Oregon, USA. Association for Computation…
22. Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. [Know what you don’t know: Unanswerable questions for SQuAD](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/). In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 784– 789, Melbourne, Australia. Association for Computational Lin…
23. Sewon Min, Victor Zhong, Richard Socher, and Caiming Xiong. 2018. [Efficient and robust question answering from minimal context over documents](https://md.research.elamur.ai/free/en/papers/min-2018-efficient-robust-question-answering-minimal/). In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1725–1735. Association for Computat…
24. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. [SQuAD: 100,000+ questions for machine comprehension of text](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/). In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383–2392. Association for Computational Linguistics.
25. Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016. A corpus and cloze evaluation for deeper understanding of commonsense stories. In Proceedings of the 2016 Conference of the North American Chapter of the Associati… [find](https://scholar.google.com/scholar?q=Nasrin+Mostafazadeh%2C+Nathanael+Chambers%2C+Xiaodong+He%2C+Devi+Parikh%2C+Dhruv+Batra%2C+Lucy+Vanderwende%2C+Pushmeet+Kohli%2C+and+James+Allen%2E+2016%2E+A+corpus+and+cloze+evaluation+for+deeper+understanding+of+commo)
26. Nasrin Mostafazadeh, Michael Roth, Annie Louis, Nathanael Chambers, and James Allen. 2017. [find](https://scholar.google.com/scholar?q=Nasrin+Mostafazadeh%2C+Michael+Roth%2C+Annie+Louis%2C+Nathanael+Chambers%2C+and+James+Allen%2E+2017%2E)
27. LSD-Sem 2017 shared task: The story cloze test. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pages 46–51. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=LSD%2DSem+2017+shared+task%3A+The+story+cloze+test%2E+In+Proceedings+of+the+2nd+Workshop+on+Linking+Models+of+Lexical%2C+Sentential+and+Discourse%2Dlevel+Semantics%2C+pages+46%E2%80%9351%2E+Association+for+Computational+Li)
28. Matthew Richardson, Christopher J.C. Burges, and Erin Renshaw. 2013. [MCTest: A challenge dataset for the open-domain machine comprehension of text](https://md.research.elamur.ai/free/en/papers/richardson-2013-mctest-challenge-dataset-open-domain/). In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 193–203.
29. Mrinmaya Sachan, Kumar Dubey, Eric Xing, and Matthew Richardson. 2015. Learning answer-entailing structures for machine comprehension. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing… [find](https://scholar.google.com/scholar?q=Mrinmaya+Sachan%2C+Kumar+Dubey%2C+Eric+Xing%2C+and+Matthew+Richardson%2E+2015%2E+Learning+answer%2Dentailing+structures+for+machine+comprehension%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+)
30. 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](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/). CoRR, abs/1611.09268.
31. Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, and Karthik Sankaranarayanan. 2018. [DuoRC: Towards complex language understanding with paraphrased reading comprehension](https://md.research.elamur.ai/free/en/papers/saha-2018-duorc-complex-language-understanding-paraphrased/). In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1683–16…
32. Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester. 2016. [Who did What: A large-scale person-centered cloze dataset](https://md.research.elamur.ai/free/en/papers/onishi-2016-who-did-what-large-scale/). In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2230– 2235. Association for Computational Linguistics.
33. Mark Sammons, V.G.Vinod Vydiswaran, and Dan Roth. 2010. [“Ask not what textual entailment can do for you...”](https://md.research.elamur.ai/free/en/papers/sammons-2010-ask-not-what-textual-entailment/). In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1199–1208. Association for Computational Linguistics.
34. Simon Ostermann, Ashutosh Modi, Michael Roth, Stefan Thater, and Manfred Pinkal. 2018. MCScript: A novel dataset for assessing machine comprehension using script knowledge. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Res… [find](https://scholar.google.com/scholar?q=Simon+Ostermann%2C+Ashutosh+Modi%2C+Michael+Roth%2C+Stefan+Thater%2C+and+Manfred+Pinkal%2E+2018%2E+MCScript%3A+A+novel+dataset+for+assessing+machine+comprehension+using+script+knowledge%2E+In+Proceedings+of+the+Eleve)
35. Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, and Noah A. Smith. 2017. Story cloze task: UW NLP system. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pages 52–55, Valencia, Spain. Association for Computational Lingu… [find](https://scholar.google.com/scholar?q=Roy+Schwartz%2C+Maarten+Sap%2C+Ioannis+Konstas%2C+Leila+Zilles%2C+Yejin+Choi%2C+and+Noah+A%2E+Smith%2E+2017%2E+Story+cloze+task%3A+UW+NLP+system%2E+In+Proceedings+of+the+2nd+Workshop+on+Linking+Models+of+Lexical%2C+Sentent)
36. Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernandez. 2016. [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://md.research.elamur.ai/free/en/papers/paperno-2016-lambada-dataset-word-prediction-requiring/). In Proceedings of the 54th Annual Meeting of the Association f…
37. Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017. [Bidirectional attention flow for machine comprehension](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/). In International Conference on Learning Representations.
38. Saku Sugawara, Yusuke Kido, Hikaru Yokono, and Akiko Aizawa. 2017. Evaluation metrics for machine reading comprehension: Prerequisite skills and readability. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 806–817. Association… [find](https://scholar.google.com/scholar?q=Saku+Sugawara%2C+Yusuke+Kido%2C+Hikaru+Yokono%2C+and+Akiko+Aizawa%2E+2017%2E+Evaluation+metrics+for+machine+reading+comprehension%3A+Prerequisite+skills+and+readability%2E+In+Proceedings+of+the+55th+Annual+Meeting+)
39. Simon Suster and Walter Daelemans. 2018. CliCR: a dataset of clinical case reports for machine reading comprehension. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1551–… [find](https://scholar.google.com/scholar?q=Simon+Suster+and+Walter+Daelemans%2E+2018%2E+CliCR%3A+a+dataset+of+clinical+case+reports+for+machine+reading+comprehension%2E+In+Proceedings+of+the+2018+Conference+of+the+North+American+Chapter+of+the+Associa)
40. A We used different hyperparameters for each dataset because of the different characteristics of the datasets, e.g., the context length. Tables 8 and 9 show the hyperparameters. Richard Sutcliffe, Anselmo Peñas, Eduard Hovy, Pamela Forner, Álvaro Rodrigo, Corina Forascu, Yassine Benajiba, and Pety… [find](https://scholar.google.com/scholar?q=A+We+used+different+hyperparameters+for+each+dataset+because+of+the+different+characteristics+of+the+datasets%2C+e%2Eg%2E%2C+the+context+length%2E+Tables+8+and+9+show+the+hyperparameters%2E+Richard+Sutcliffe%2C+Ans)
41. Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017. [NewsQA: A machine comprehension dataset](https://md.research.elamur.ai/free/en/papers/trischler-2017-newsqa-machine-comprehension-dataset/). In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 191–200. Association for Computational Linguistics.
42. Dirk Weissenborn, Georg Wiese, and Laura Seiffe. 2017. Making neural QA as simple as possible but not simpler. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 271–280, Vancouver, Canada. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Dirk+Weissenborn%2C+Georg+Wiese%2C+and+Laura+Seiffe%2E+2017%2E+Making+neural+QA+as+simple+as+possible+but+not+simpler%2E+In+Proceedings+of+the+21st+Conference+on+Computational+Natural+Language+Learning+%28CoNLL+2)
43. Dataset b h q d SQuAD AddSent NewsQA TriviaQA QAngaroo MARCO NarrativeQA 60 60 32 32 16 20 60 100 100 100 100 50 40 50 400 400 1000 400 4096 1600 1000 20 20 20 20 20 30 20 [find](https://scholar.google.com/scholar?q=Dataset+b+h+q+d+SQuAD+AddSent+NewsQA+TriviaQA+QAngaroo+MARCO+NarrativeQA+60+60+32+32+16+20+60+100+100+100+100+50+40+50+400+400+1000+400+4096+1600+1000+20+20+20+20+20+30+20)

*Updated 24 October 2026*