The DOI matches its Crossref record; 22 of the 24 references it cites were found in a registry or an index.
NewsQA: A Machine Comprehension Dataset
Open accessAbstract
We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text in the articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. Analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (13.3% F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available online.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 1×
“Existing datasets vary in size, difficulty, and collection methodology; however, as pointed out by Rajpurkar et al. (2016), most suffer from one of two shortcomings: those that are designed explicitly to test comprehension (Richardson et al.”
BackgroundGlove: 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×
“Word embeddings are initialized using GloVe vectors (Pennington et al., 2014) pre-trained on the 840-billion Common Crawl corpus.”
BackgroundMethodBLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · cited 1×
“BLEU is a precision-based metric popular in machine translation that uses a weighted average of variable length phrase matches ( n -grams) against the reference sentence (Papineni et al., 2002).”
MethodSQuAD: 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 3×
“Existing datasets vary in size, difficulty, and collection methodology; however, as pointed out by Rajpurkar et al. (2016), most suffer from one of two shortcomings: those that are designed explicitly to test comprehension (Richardson et al., 2013) are too small for training data-intensive deep…”
BackgroundMethodInfluentialCIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845 · cited 2×
“ase matches (n-grams) against the reference sentence (Papineni et al., 2002). CIDEr was designed to correlate better with human judgements of sentence similarity, and uses tf-idf scores over n-grams (Vedantam et al., 2015). As given in Table 4, humans averaged 0.694 F1 on NewsQA. The human EM scores are relatively low…”
MethodInfluentialOn the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5063 · Cited by 4,310
Teaching Machines to Read and ComprehendPreprint
Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,855
Liu, C., Lowe, R., Serban, I. V., Noseworthy, M., Charlin, L., & Pineau, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1230 · Cited by 1,377
Exact solutions to the nonlinear dynamics of learning in deep linear neural networksPreprint
Saxe, A., McClelland, J. L., & Ganguli, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.6120 · Cited by 1,010
MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
Richardson, M., Burges, C., & Renshaw, E. (2013) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1020 · Cited by 778 · cited 5×
“Like Richardson et al. (2013) we want to encourage reasoning in comprehension models.”
BackgroundInfluential
Research works printed but not linked: 3
- NewsQA: A Machine Comprehension Dataset https://aclanthology.org/W17-2623/ (Trischler et al., RepL4NLP 2017)
- Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017. NewsQA: A Machine Comprehension Dataset. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 191–200, Vancouver, Canada. Association for Computational Linguistics.
- ACL materials are Copyright © 1963–2026 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
Cited by
Sorted by citations
Items 601–700 of 708
Superimposed Attention Mechanism-Based CNN Network for Reading Comprehension and Question Answering
Li, M., Hou, X., Jiao-e, L., & Gao, K. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-0121-0_2
Neural architectures for natural language understanding
Tay, Y. (2019) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/83291
Reconstructed Option Rereading Network for Opinion Questions Reading Comprehension
Qiu, D., Bao, L., Tian, Z., Zhang, Y., Liu, K., Zhao, J., & Liao, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32381-3_8
“Differing from extractive machine reading comprehension datasets such as SQuAD [8]and NewsQA [15], the correct answer for most questions in MCRC may not directly appear in the original passage.”
MethodMeta Answering for Machine ReadingPreprint
Börschinger, B., Boyd-Graber, J. L., Buck, C., Bulian, J., Ciaramita, M., Huebscher, M. C., Gajewski, W., Kilcher, Y., Nogueira, R. F., & Saralegui, L. S. (2019) · arXiv (Cornell University) · doi:10.3929/ethz-b-000393755 · Cited by 1
Li, J., Liu, M., Qin, B., Zheng, Z., & Liu, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03514 · Cited by 1
“There are several types of datasets for machine comprehension, including extractive datasets (Joshi et al., 2017; Trischler et al., 2017), answer sentence selection datasets (Wang et al.”
Recurrent Chunking Mechanisms for Conversational Machine Reading Comprehension
Gong, H., Shen, Y., Yu, D., Chen, J., & Yu, D. (2019)
Which of the Following cannot be Inferred from the above Passage?
Guven, T., Cebeci, G., & Amasyalı, M. F. (2019) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu48272.2019.8946369
“NewsQA [7] veri kümesi, gazete haberlerinden toplanan metinlere karşılık insanlar (crowdworkers) tarafıdan hazırlanan soru ve cevaplar ile oluşturulmuştur.”
A Review of Neural Approaches to the Question Answering Task.
Needham, W. P. (2019)
Practical Annotation Strategies for Question Answering DatasetsPreprint
Kratzwald, B., Yue, X., Sun, H., & Feuerriegel, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03235 · Cited by 1
“This analysis is based on SQuAD and NewsQA; the reason is that these datasets contain multiple question-answer annotations for a single context document.”
BackgroundMethodInfluentialForecastQA: Machine Comprehension of Temporal Text for Answering Forecasting QuestionsPreprint
Jin, W., Kim, S., & Ren, X. (2020) · arXiv (Cornell University)
Verb Focused Answering from CORD-19
George, E. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58323-1_22
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading ComprehensionPreprint
Luo, H., Shi, Y., Gong, M., Shou, L., & Li, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.14348
“We also evaluate our strategy on three other datasets from the MRQA 2019 Shared Task2: NewsQA (Trischler et al., 2017), HotpotQA (Yang et al., 2018), Natural Questions (Kwiatkowski et al., 2019).”
MethodMulti-model transfer and optimization for cloze task
Tang, J., Ling, L., Ma, C., Zhang, H., & Huang, J. (2020) · International Symposium on Artificial Intelligence and Robotics (ISAIR) · doi:10.1117/12.2579412
Liu, C.-L., & Lee, H.-Y. (2020) · arXiv (Cornell University)
Cross-lingual Training for Multiple-Choice Question Answering
Echegoyen, G., Rodrigo, Á., & Peñas, A. (2020) · RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) · doi:10.26342/2020-65-4
“The authors found a higher ratio of reasoning questions with respect to CNN (Hermann et al., 2015), SQUAD and NEWSQA (Trischler et al., 2017), which jus-tifies that RACE is more difficult than those datasets.”
ResultInformation Extraction from Public Meeting Articles
Virgo, F. G., Chu, C., Ogawa, T., Tanaka, K., Ashihara, K., Nakashima, Y., Takemura, N., Nagahara, H., & Fujikawa, T. (2022) · SN Computer Science · doi:10.1007/s42979-022-01176-z
“NewsQA [26] is an extractive reading comprehension dataset consisting of 100 k question-answer pairs created by crowdworkers based on 10 k CNN news articles.”
MethodImportance of the Single-Span Task Formulation to Extractive Question-answering
Xu, M.-A., & Khanna, R. (2020) · doi:10.5121/csit.2020.101809 · Cited by 1
“Numerous datasets have been created with several answer categories, NewsQA [2], DuReader [3], MS MARCO [4], DROP [5], however, the majority of the answers are single-span.”
MethodSentence Answer Selection for Open Domain Question Answering via Deep Word Matching
Ghigi, F., Turcsány, D., Kaltenbrunner, T., & Cibelli, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66527-2_21
Evaluation of Single-Span Models on Extractive Multi-Span Question-Answering
Xu, M.-A., & Khanna, R. (2021) · International journal of Web & Semantic Technology · doi:10.5121/ijwest.2021.12102
“Numerous datasets have been created with several answer categories, NewsQA [2], DuReader [3], MS MARCO [4], DROP [5], however, the majority of the answers are single-span.”
MethodDai, D., Zheng, H., Sui, Z., & Chang, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.14443
“2016) and NewsQA (Trischler et al., 2017).”
When to Fold'em: How to answer Unanswerable questionsPreprint
Ho, M., Zhou, Z., & He, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.00328
“There are also a variety of benchmarks related to question-answering – NewsQA [12], SearchQA [13], TriviaQA [14], HotpotQA [15], and Natural Questions [16].”
BackgroundResearch on Equivalence Discrimination of Social Security Problems
Cao, C., & He, Y. J. (2020) · International Conference on Mechanical, Control and Computer Engineering (ICMCCE) · doi:10.1109/icmcce51767.2020.00499
Learning to Generate Task-Specific Adapters from Task DescriptionPreprint
Ye, Q., & Ren, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00420
“, 2019) and NewsQA (Trischler et al., 2017), and use these as out-of-domain test examples.”
BackgroundMethodInfluentialKeep it Simple: Unsupervised Simplification of Multi-Paragraph TextPreprint
Laban, P., Schnabel, T., Bennett, P. N., & Hearst, M. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03444 · Cited by 5
“Questions were generated via a GPT2 question generation model fine-tuned on the NewsQA dataset (Trischler et al., 2017).”
MethodGeoCQA: A Large-Scale Geography-Domain Chinese Question Answering Dataset from Examination
Cui, Z. S., Wang, B., & Ju, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88483-3_13
A Flexible and Extensible Framework for Multiple Answer Modes Question Answering.
Fan, C.-C., Kuo, C.-C., Luo, S.-B., Liao, P.-J., Chang, K.-Y., Hsu, C.-W., Wu, M.-T., Tsai, S.-H., Wu, T.-M., Smolka, A., et al. (2021) · International Conference on Computational Linguistics
CodeQA: A Question Answering Dataset for Source Code ComprehensionPreprint
Liu, C., & Wan, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08365 · Cited by 14
“In recent years, QA has been applied into broad application domains, such as news (Hermann et al., 2015; Trischler et al., 2016), science (Khot et al., 2018; Hardalov et al., 2020), movies (Miller et al., 2016), medical field (Pampari et al., 2018), etc.”
Automatically Solving Elementary Science Questions: A Survey
Nagdev, S., Radke, M. A., & Ramanath, M. (2022) · IETE Technical Review · doi:10.1080/02564602.2022.2048716
“For the Extractive type questions, SQuAD 1.1 [56], SQuAD 2 [57], NewsQA [58], Quoref [59], ROPES [60] are used.”
MethodInfluentialA Question-Oriented Propagation Network for News Reading Comprehension
Wen, L., Wang, H., Ma, D., Fan, J., Luo, Y., Wang, X., Shi, D., Cheng, Z., & Yin, D. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747142
Lin, Y., Liang, D., & Kang, X. (2022) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3532213.3532241
“Different from other datasets of MRC such as SQuAD [3] and NewsQA [4] which directly get answers from the original text, the answers of multiple-choice MRC need to be obtained by reasoning and summarizing the relevant fragments of the original text, as shown in Figure 1.”
MethodSiamese Q&A: Distinguishing Unanswerable Questions Using Deep Learning
Padmanabhan, L. A., & Vadivu, G. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-2069-1_53
A Semi-automatic Data Generator for Query Answering
Angiulli, F., Prete, A. D., Fassetti, F., & Nisticò, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16564-1_11
An In-depth Interactive and Visualized Platform for Evaluating and Analyzing MRC Models
Wu, Z., Fang, J., Xu, H., & Gao, K. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557167
Information Extraction Based on Multi-turn Question Answering for燗nalyzing Korean Research Trends
Jo, S., Oh, H., Im, S., Kim, G., & Kim, S. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.031983
Elema, A. M. (2022) · International Conference on Information and Communication Technology for Development for Africa (ICT4DA) · doi:10.1109/ict4da56482.2022.9971413
“[10],[11] and [12] have also been investigated.”
Phan, P. X. (2022) · VNU Journal of Science Computer Science and Communication Engineering · doi:10.25073/2588-1086/vnucsce.364
Alexandrov, D., Zakharova, A., & Butakov, N. A. (2023) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc56153.2023.00012
“1 [4], Natural Questions (NQ) [5] and NewsQA [6].”
BackgroundMethodЗАГАЛЬНОДОСТУПНІ НАБОРИ ДАНИХ ТА МЕТРИКИ ДЛЯ СПРИЯННЯ ДОСЛІДЖЕННЯМ СИСТЕМ НАДАННЯ ВІДПОВІДЕЙ
Vyshniak, M. Y., & Pyrozhenko, M. Y. (2023) · Таврійський науковий вісник Серія Технічні науки · doi:10.32851/tnv-tech.2023.1.2
Entity Level QA Pairs Dataset for Sentiment Analysis
Lahiri, A. K., & Hu, Q. (2022) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat55865.2022.00046
“Traditionally, Conditional Random Fields (CRF) have been widely explored [36] [42] [43].”
BackgroundSADE: A Self-Adaptive Expert for Multi-Dataset Question Answering
Peng, Y.-X., Wang, Q., Mao, Z.-D., & Zhang, Y. (2023) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp49357.2023.10097208 · Cited by 2
Nguyễn, D. A., Li, M., Lambert, G. W., Kowalczyk, R., McDonald, R. L., & Vo, Q. B. (2023) · doi:10.2196/preprints.48539
MSQA: An Unsupervised Domain-Adapted Question Answering Method Based on Multiple Source Domains
Peng, T., Zhang, X., Huang, J., Liu, J., Hu, X., & He, R. (2023) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops56833.2023.10150310
“We used five in-domain datasets from the MRQA shared task [6] as training datasets, which are SQuAD [26], NewsQA [32], TriviaQA [10], SearchQA [5] and HotpotQA [37].”
MethodMulti-source Machine Reading Comprehension with Meta-Learning and Adaptive Adversarial Training
Wen, Z., Qi, Y., Liu, H., Lin, Z., & Wang, W. (2023) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2023-179
A Novel Question-Context Interaction Method for Machine Reading Comprehension
Phan, T.-A., Ngo, H., & Bui, K. N. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-5834-4_5
Uniform Training and Marginal Decoding for Multi-Reference Question-Answer Generation
Vakulenko, S., Byrne, B., & de Gispert, A. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230539
“To produce this benchmark, we use: • the test and train splits of NewsQA [15], which contains manually produced QA pairs for sentences from news articles; • Stanza English for NER 2 ; • T5 base QAG model fine-tuned on the train split of NewsQA with a greedy decoding strategy.”
MethodFang, J., Xu, H., Wu, Z., Gao, K., Che, X., & Hui, H. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200287
Improving Open-Domain Answer Sentence Selection by Distributed Clients with Privacy Preservation
Wang, W., Shen, T., Blumenstein, M., & Long, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46677-9_2
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data
Adiba, A. I., Homma, T., & Sogawa, Y. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.42
“A number of large corpora have played a critical role in advancing MRC research (Rajpurkar et al., 2016; Trischler et al., 2017; Ba-jaj et al., 2016; Zhang et al., 2018).”
BackgroundKeyword Provision Question Generation for Facilitating Educational Reading Comprehension Preparation
Chan, Y.-H., Chung, H.-L., & Fan, Y.-C. (2022) · International Conference on Natural Language Generation · doi:10.18653/v1/2022.inlg-main.15
“Current data-driven QG models are trained with factoid QA datasets (e.g., SQuAD (Rajpurkar et al., 2016) or NewsQA (Trischler et al., 2016)), and therefore generate factoid questions, which are too simple for advanced reading practice assessment.”
MethodMTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document
Chu, Z., Wang, Z., Liang, J., Liu, M., & Qin, B. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1016
Few-shot Unified Question Answering: Tuning Models or Prompts?
Bansal, S., Yavuz, S., Pang, B., Bhat, M., & Zhou, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.550 · Cited by 3
Machine Reading Comprehension using Case-based Reasoning
Thai, D., Agarwal, D., Chaudhary, M., Zhao, W., Das, R., Lee, J. Y., Hajishirzi, H., Zaheer, M., & McCallum, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.564 · Cited by 4
Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading
Luo, Y., Tian, S., Yuan, C., & Wang, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.866
Makahleh, M., Obiedat, R., & Quwaider, M. (2023) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams60348.2023.10375456
“Additionally, NewsQA, introduced by Trischler et al. [17], incorporates 119,633 questions and answers based on 12,744 CNN news articles generated by crowdworkers.”
MethodVietLegalQA: Unsupervised Legal Question Answering for Vietnamese Using Cloze Translation Approach
Ngoc, K. N., Trong, P. N., Minh, C. L. N., Khang, N. H. G., Quoc, T. N., & Hoang, V. T. (2023) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn59264.2023.10402152
SpanBERT-based Multilayer Fusion Model for Extractive Reading Comprehension
Zhang, P., He, L., & Xi, D. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150149 · Cited by 1
“The commonly used datasets for extractive reading comprehension include the SQuAD dataset [1], the NewsQA dataset [2], the TriviaQA dataset [3], and so on.”
MethodImpacts of Multi-task Adversarial Training on Machine Reading Comprehension Model
Yan, R., Du, Y., Wang, B., & Guo, W. (2023) · International Conference on Network and Information Systems for Computers (ICNISC) · doi:10.1109/icnisc60562.2023.00120
Enhancing Low-Resource Question-Answering Performance Through Word Seeding and Customized Refinement
Pandya, H. A., & Bhatt, B. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.01503138
“Moreover, the noun phrases in a given passage are the most plausible answer to the asked question [23], [25].”
BackgroundA Review on Conversational Question Answering (CQA)
Tonde, M. J., & Sankaye, S. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-16960
LogSay: An Efficient Comprehension System for Log Numerical Reasoning
Qi, J., Luan, Z., Huang, S., Fung, C., & Yang, H. (2024) · IEEE Transactions on Computers · doi:10.1109/tc.2024.3386068 · Cited by 1
“…♣ Hard SQuAD [1] Wikipedia ♣ Simple HotpotQA [14] Wikipedia ♠♣ Medium Drop [2] Wikipedia ♣ Medium 2WikiMultiHopQA [15] Wikipedia ♠♣ Hard NewsQA [16] News ♣ Simple JEC-QA [8] Legal ♣ Hard Event-QA [9] Knowledge Graphs ♠♣ Medium COVID-QA [10] Medicine ♣ Simple BiRdQA [11] Riddle ♠♣ Hard *…”
CollabAS2: Enhancing Arabic Answer Sentence Selection Using Transformer-Based Collaborative Models
Aouichat, A., & Guessoum, A. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09345-3
Extractive Question Answering for Spanish and Arabic Political Text
Alsarra, S., Whitehead, P., Alatrush, N., Abdeljaber, L., Khan, L. R., Osorio, J., Brandt, P. T., & D'Orazio, V. J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72241-7_14
Learning to generate text with auxiliary tasks
Hùng, P. Q., Nguyen, M.-T., Inoue, S., Tran-Tien, M., & Phan, X.-H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112570
Compressed Models are NOT Miniature Versions of Large Models
Raj, R., Pal, R., & Awekar, A. C. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679888
LLM Assists Hypothesis Generation and Testing for Deliberative Questions
Wang, F., Zhou, X., Hu, W., Luo, Z., Luo, W. P., & Bai, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9434-8_33 · Cited by 1
Improved bidirectional attention flow (BIDAF) model for Arabic machine reading comprehension
Biltawi, M. M., Awajan, A., & Tedmori, S. (2024) · Natural language processing. · doi:10.1017/nlp.2024.46
Li, B., Tang, J., Yu, Q., Xu, Y., Lu, Y., Wang, K., Li, L., & Zhou, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8367-0_13
PPDAC: A Plug-and-Play Data Augmentation Component for Few-Shot Extractive Question Answering
Huang, Q., Fu, H., Luo, W., Wang, M., & Luo, K. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8367-0_28 · Cited by 1
An Analysis of Automatic Question Generation Research Progress and Challenges
Dey, D., & Rudrapal, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8336-6_19
Denoising Implicit Feedback for Extractive Question Answering
Hu, X., Chen, J., Huang, Z., Yao, X., & Yang, J. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-2681-6_27
Learning how to transfer: A lifelong domain knowledge distillation framework for continual MRC
Li, S., Wu, Z., Cao, R., Zhang, X., Wang, Y., Xu, H., & Gao, K. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2025.200497 · Cited by 1
Audiopedia: Audio QA with Knowledge
Penamakuri, A. S., Chhatre, K., & Jain, A. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889814 · Cited by 2
“…modality of the input, question answering in the literature can be categorised into four coarse segments, (i) Textual Question Answering (TQA) [19]–[21]: question to be answered over a textual paragraph, (ii) Visual Question Answering (VQA) [22]–[25]: question to be answered over an image, (iii)…”
KPQDG: a key sentence prompt-tuning for automatic question-distractor pairs generation
Yin, Z., & Li, L. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11149-x · Cited by 2
“Examples of such datasets include SQuAD [44], NewsQA [45], and MS MARCO [46].”
Binary mask tuning on gradient: Towards multi-data question answering
Gong, C., Wei, Z., Zhu, P., & Miao, D. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113505
Guleria, N., Mahajan, S., & Pandey, P. K. (2025) · Advances in logistics, operations, and management science book series · doi:10.4018/979-8-3693-7620-1.ch010
ViEduQA: A New Vietnamese Dataset for Question Answer Generation in Education
Nguyen, T.-P., Le, H.-L., Hùng, P. Q., Huy, N. Q., Phan, X.-H., & Nguyen, M.-T. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4288-5_34
Multi-Model System for Enhanced QA: Contextual Answering, Profanity Filtering, and Pair Generation
Rao, K. V., Srinivas, C. S., Chimaladinne, V. V., Devarakonda, B. P., & Lalitha, A. S. (2025) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss63372.2025.11076248
“The Surge AI Toxicity Dataset[14] comprises 1,632 comments from various social media platforms, evenly split between both toxic and non-toxic entries.”
Relia, L., Patel, J., Panchal, N. A., & Garg, D. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-3420-0_17
UQuAD+: Benchmark Dataset for Urdu Machine Reading Comprehension
Kazi, S., & Khoja, S. A. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3759455
“Domain-specific datasets have further advanced MRC capabilities: NewsQA targets real-world comprehension from news articles [60], while TriviaQA evaluates reasoning across multiple sources [26].”
Inspire Me with Your Questions: Repurposing Historical Questions for New Documents
Liu, Y., Cao, Y., & Luo, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02088-8_6
Question Answering in a Low-Resource Language: Dataset and Deep Learning Adaptations for Sinhala
Ranasinghe, J., & Weerasinghe, R. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-04339-9_22
MIRAGE: A Metrics lIbrary for Rating hAllucinations in Generated tExt
Vendeville, B., Магдановна, Е. Л., Loor, P. D., & Kamps, J. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761644 · Cited by 2
Efficient Knowledge Transfer from Large to Small Language Models via Low-Overhead Query Mechanism
Ahemad, F. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761417 · Cited by 1
Moradisani, H., Zarrinkalam, F., Noorian, Z., & Ensan, F. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11421-5
CebQA: Cebuano Question Answering System
Lagumbay, J. R. T., & Roxas, R. R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10827-2_28
Dementiaguard - Integrated Digital Healthcare Solution for Dementia Care
Jayaweera, J. A. V. T., Ranasinghe, R. A. H. B., Perera, K. A. S. N., Shabeer, M. S. M., Chandrasiri, S., & Kasthurirathna, D. (2025) · International Conference on Advancements in Computing (ICAC) · doi:10.1109/icac69156.2025.11361481
“Two datasets, SQaUD’ [23] and ‘NewsQA’ [24] were used to fine-tune the T5 model.”
Liu, H. T., Zhang, Y., Meng, Q., & Zhang, X. (2025) · International Conference on Big Data Analytics and Artificial Intelligence Application · doi:10.1145/3788108.3788109
“Notably, bench mark datasets such as SQuAD [2], Narrative QA [3],NewsQA [4], and TriviaQA [5] have demonstrated utility across various do mains.”
A Spanish Language Proficiency Dataset for AI Evaluation
Peñas, A., Rodrigo, Á., Giménez, J. F., Pastor, I. S., Moreno‐Álvarez, S., Pérez, A., & Reyes-Montesinos, J. (2026) · Information · doi:10.3390/info17020159
TransformKV: Optimizing Multi-Turn Conversational Services in LLMs via KV Cache Transformation
Zhou, J., Fang, Z., Qin, Y., Chen, W., Zhang, T., Zhang, C., & Zheng, Z. (2026) · IEEE Transactions on Computers · doi:10.1109/tc.2026.3678524
“To assess output quality, we utilize the SQuAD 2.0 [26], RACE [27], and NewsQA [28] datasets.”
InfluentialVers la mitigation des biais en traitement neuronal des langues
Berre, G. L. (2023) · theses.fr (ABES) · doi:10.70675/f37c3802z821az4a6dzb877zf631994cacd5
FlowRAG: Continual Learning for Dynamic Retriever in Retrieval-Augmented Generation
Zhang, S., Shi, T., Song, D., Zhang, L., Zhang, S., Liao, X., & Jin, H. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792361 · Cited by 2
MegaTempQA: A Million-Scale Temporal Question-Answer Dataset for Reducing LLM Hallucinations
Javed, H., Muhammad, K., Oh, H., & Ali, F. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461542
Gao, Z., & Liu, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116003
Membership Inference Attacks for Retrieval Based In-Context Learning for Document Question Answering
Kulkarni, T., Koskela, A., & Zumot, L. (2026) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml68715.2026.00062
“3) Compute the conditional log probabilities log(Pr t i +1 | t : i ) for the true tokens on LM r for each prefix ∀ i ∈ I ∪ { n } and populate them in the ordered set L r .”
Multi-Domain Question Answering System with Reinforcement Learning Based Retriever Selection
O’Rourke, N., Eaman, A., Hassan, E., & Shakshuki, E. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.04.045
CalibLoop: Self-Calibrating Pseudo-Label Learning for Noise-Robust Source-Free QA Domain Adaptation
Shi, C., Zhang, T., Yang, Z., Aysa, A., & Ubul, K. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3435-6_6
Metadata
- Authors
- Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., & Suleman, K.
- Venue
- Proceedings of the 2nd Workshop on Representation Learning for NLP
- Published
- 2017
- Pages
- 191-200
- OpenAlex
- W2557764419
- Cited by
- 706
- References
- 24
- Citations by year
- 2026: 18 · 2025: 26 · 2024: 39 · 2023: 96 · 2022: 110 · 2021: 134 · 2020: 138 · 2019: 87 · 2018: 41 · 2017: 16
- FWCI
- 35.17
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Representation Learning for NLP (RepL4NLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- machine reading comprehension, question answering, news articles, exploratory questions, reasoning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
