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Cited by 427Topic ModelingNatural Language Processing TechniquesReading and Literacy Development

The DOI matches its Crossref record; 51 of the 53 references it cites were found in a registry or an index.

DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Open access
Dua, Dheeru, Wang, Yizhong, Dasigi, Pradeep, Stanovsky, Gabriel, Singh, Sameer, Gardner, Matt·2019·Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
DOI 10.18653/v1/n19-1246
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Abstract

Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

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Crossref · 10.18653/v1/n19-1246

Cites

51 of 51 references verified0 citation errors · 0 not checkable

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Citations

Cites

  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

    Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325 · cited 4×

    “For example, BERT, the current state-of-the-art on SQuAD, drops by more than 50 absolute F1 points.”
    MethodInfluential

  • Glove: Global Vectors for Word Representation

    Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · cited 2×

    “We also add context-specific rules to produce strings occurring in both question and paragraph, and those paragraph strings that are neighbors of question tokens in the GloVe embedding space (Pennington et al., 2014), up to a cosine distance of d .”
    Method

  • Deep Contextualized Word Representations

    Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · cited 2×

    “We test four different SQuAD-style reading comprehension models on DROP: (1 (Peters et al., 2018) to the original embeddings (78.7% EM); (4) BERT (Devlin et al., 2018), which recently achieved improvements on many NLP tasks with a novel pre-training technique (84.7% EM).”
    MethodInfluentialSelf-citation

  • SQuAD: 100,000+ Questions for Machine Comprehension of Text

    Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · cited 1×

    “Question answering datasets: With systems reaching human performance on the Stanford Question Answering Dataset (SQuAD) (Rajpurkar et al., 2016), many follow-on tasks are currently being proposed.”
    Method

  • A large annotated corpus for learning natural language inference

    Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377 · cited 3×

    “…et al., 2018; Kaushik and Lipton, 2018) has identified that popular synthetic NLP datasets (such as SQuAD (Ra-jpurkar et al., 2016) or SNLI (Bowman et al., 2015)) are prone to have artifacts and annotation biases which can be exploited by supervised algorithms that learn to pick up these…”
    BackgroundInfluential

  • Know What You Don’t Know: Unanswerable Questions for SQuAD

    Rajpurkar, P., Jia, R., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2124 · Cited by 2,240 · cited 2×

    “, 2018; Talmor and Berant, 2018), mismatched passages and questions (Saha et al., 2018; Kociský et al., 2018; Rajpurkar et al., 2018), integrating knowledge from external sources (Mihaylov et al.”
    Background

  • HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

    Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804 · cited 2×

    “…challenge, around tracking conversational state (Reddy et al., 2018; Choi et al., 2018), requiring passage retrieval (Joshi et al., 2017; Yang et al., 2018), mis-matched passages and questions (Saha et al., 2018; Kocisk´y et al., 2018; Rajpurkar et al., 2018), integrating knowledge from…”
    Background

  • Bidirectional Attention Flow for Machine ComprehensionPreprint

    Seo, M. J., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01603 · Cited by 1,698 · cited 3×

    “…instructions given to workers, and partially through the use of an adversarial baseline : we ran a base-line reading comprehension method (BiDAF) (Seo et al., 2016) in the background as crowd workers were writing questions, requiring them to give questions that the baseline system could not…”
    MethodInfluential

  • TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

    Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690 · cited 2×

    “…to the reading comprehension challenge, around tracking conversational state (Reddy et al., 2018; Choi et al., 2018), requiring passage retrieval (Joshi et al., 2017; Yang et al., 2018), mis-matched passages and questions (Saha et al., 2018; Kocisk´y et al., 2018; Rajpurkar et al., 2018),…”
    Background

  • Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

    Mihaylov, T., Clark, P. E., Khot, T., & Sabharwal, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1260 · Cited by 1,602 · cited 1×

    “, 2018), integrating knowledge from external sources (Mihaylov et al., 2018; Zhang et al., 2019), tracking entity state changes (Mishra et al.”
    Background

Show all 53 references

Cited by

Sorted by citations

  • The Power of Scale for Parameter-Efficient Prompt Tuning

    Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092

    “Of the datasets where model tuning is better, we see that DROP shares a domain (Wikipedia) with SQuAD and is thus one of the smallest domain transfers.”
    Background

  • The Power of Scale for Parameter-Efficient Prompt Tuning

    Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096

    “Of the datasets where model tuning is better, we see that DROP shares a domain (Wikipedia) with SQuAD and is thus one of the smallest domain transfers.”
    Background

  • PaLM: Scaling Language Modeling with Pathways

    Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590

    “…Common Sense Reasoning: PIQA (Bisk et al., 2019), ARC (Clark et al., 2018), OpenBookQA (Mihaylov et al., 2018) • In-context Reading Comprehension: DROP (Dua et al., 2019), CoQA (Reddy et al., 2018), QuAC (Choi et al., 2018), SQuADv2 (Rajpurkar et al., 2018), RACE (Lai et al., 2017) •…”
    Background

  • Finetuned Language Models Are Zero-Shot Learners

    Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101

    “0 -20 20 40 60 Percent of Data Clean in Dataset Percent change in performance of FLAN (accuracy, F1, or BLEU) eval on only clean data did better eval on all data (including dirty) did better ANLI R2 SQuAD v2 ANLI R1 DROP ReCoRD PIQA 25 500 75 100 Figure 9: Like GPT-3, we also measured performance on cleaned versions o…”
    BackgroundInfluential

  • Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data

    Bender, E. M., & Koller, A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.463 · Cited by 1,082

    “For example, the DROP reading comprehension benchmark (Dua et al., 2019) seeks to create more stringent tests of understanding by creating questions that require the system to integrate information from different parts of a paragraph via simple arithmetic or similar operations.”
    Background

See all 427

Related

Metadata

Authors
Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., & Gardner, M.
ORCID
Venue
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Publisher
Association for Computational Linguistics
Published
2019
Pages
2368-2378
DOI
10.18653/v1/n19-1246
OpenAlex
W2919420119
Cited by
393
References
53
Citations by year
2026: 20 · 2025: 8 · 2024: 20 · 2023: 60 · 2022: 62 · 2021: 113 · 2020: 78 · 2019: 32
FWCI
27.26
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
Keywords
reading comprehension, benchmark, question answering

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