Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

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

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

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

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818

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

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

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473

Learning deep structured semantic models for web search using clickthrough data

Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. P. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505665 · Cited by 2,120

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

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

Reading Wikipedia to answer open-domain questions

Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1171 · Cited by 1,546

Dense passage retrieval for open-domain question answering

Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575

A Survey on Automated Fact-Checking

Guo, Z., Schlichtkrull, M., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00454 · Cited by 500

Fine-grained Fact Verification with Kernel Graph Attention Network

Liu, Z., Xiong, C., Sun, M., & Liu, Z. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.655 · Cited by 236

Hierarchical Graph Network for Multi-hop Question Answering

Fang, Y., Sun, S., Gan, Z., Pillai, R., Wang, S., & Liu, J. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.710 · Cited by 176

Coreferential Reasoning Learning for Language Representation

Ye, D., Lin, Y., Du, J., Liu, Z., Li, P., Sun, M., & Liu, Z. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.582 · Cited by 181

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Asai, A., Hashimoto, K., Hajishirzi, H., Socher, R., & Xiong, C. (2019) · arXiv · doi:10.48550/arxiv.1911.10470 · Cited by 152

Retrieving and Reading: A Comprehensive Survey on Open-domain Question AnsweringPreprint

Zhu, F., Lei, W., Wang, C., Zheng, J., Poria, S., & Chua, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00774 · Cited by 343

Unsupervised Question Decomposition for Question Answering

Perez, E., Lewis, P., Yih, W.-T., Cho, K., & Kiela, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.713 · Cited by 123

CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

Liu, Y., Li, H., Cheng, Y., Ray, S., Huang, Y., Zhang, Q., Du, K., Yao, J., Lu, S., Ananthanarayanan, G., et al. (2024) · Proceedings of the ACM SIGCOMM 2024 Conference · doi:10.1145/3651890.3672274 · Cited by 118

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Asai, A. (2025) · TIB Data Manager · doi:10.57702/2jfyctjr · Cited by 109

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

Xiong, W., Li, X. L., Iyer, S., Du, J., Lewis, P., Wang, W. Y., Mehdad, Y., Yih, W.-T., Riedel, S., Kiela, D., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.12756 · Cited by 112

HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification

Jiang, Y., Bordia, S., Zhong, Z., Dognin, C., Singh, M., & Bansal, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.309 · Cited by 173

Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention

Zhao, C., Xiong, C., Rosset, C., Xia, S., Bennett, P. N., & Tiwary, S. (2020) · International Conference on Learning Representations · Cited by 94

A Survey on Machine Reading Comprehension Systems

Baradaran, R., Ghiasi, R., & Amirkhani, H. (2022) · Natural Language Engineering · doi:10.1017/s1351324921000395 · Cited by 108

Multilingual Autoregressive Entity Linking

Cao, N. D., Wu, L., Popat, K., Artetxe, M., Goyal, N., Plekhanov, M., Zettlemoyer, L., Cancedda, N., Riedel, S., & Petroni, F. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00460 · Cited by 107

Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation

Chen, X., Meng, F., Li, P., Chen, F., Xu, S., Xu, B., & Zhou, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.275 · Cited by 77

The state of human-centered NLP technology for fact-checking

Das, A., Liu, H., Kovatchev, V., & Lease, M. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103219 · Cited by 89

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

Zhang, J., Hashimoto, K., Liu, W., Wu, C.-S., Wan, Y., Yu, P. S., Socher, R., & Xiong, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.411 · Cited by 81

GERE: Generative Evidence Retrieval for Fact Verification

Chen, J., Zhang, R., Guo, J., Fan, Y., & Cheng, X. (2022) · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3531827 · Cited by 66

ProoFVer: Natural Logic Theorem Proving for Fact Verification

Krishna, A., Riedel, S., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00503 · Cited by 51

Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Yadav, V., Bethard, S. J., & Surdeanu, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.414 · Cited by 57

A Simple Yet Strong Pipeline for HotpotQA

Groeneveld, D., Khot, T., Mausam, & Sabharwal, A. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.711 · Cited by 48

A Review on Fact Extraction and Verification

Bekoulis, G., Papagiannopoulou, C., & Deligiannis, N. (2023) · ACM Computing Surveys · doi:10.1145/3485127 · Cited by 48

TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference

Ye, D., Lin, Y., Huang, Y., & Sun, M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.463 · Cited by 68

Exploring Listwise Evidence Reasoning with T5 for Fact Verification

Jiang, K., Pradeep, R., & Lin, J. (2021) · doi:10.18653/v1/2021.acl-short.51 · Cited by 46

Recent Advancements in Artificial Intelligence Technology: Trends and Implications

Vinothkumar, J., & Karunamurthy, A. (2023) · Quing International Journal of Multidisciplinary Scientific Research and Development · doi:10.54368/qijmsrd.2.1.0003 · Cited by 55

Answering Open-Domain Questions of Varying Reasoning Steps from Text

Qi, P., Lee, H., Sido, O. T., & Manning, C. D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.292 · Cited by 65

HHGN: A Hierarchical Reasoning-based Heterogeneous Graph Neural Network for fact verification

Chen, C., Cai, F., Hu, X., Chen, W., & Chen, H. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102659 · Cited by 38

DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding

Nie, P., Zhang, Y., Geng, X., Ramamurthy, A., Song, L., & Jiang, D. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401271 · Cited by 44

Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification

Si, J., Zhou, D., Li, T., Shi, X., & He, Y. (2021) · doi:10.18653/v1/2021.acl-long.128 · Cited by 38

Semantic matching in machine reading comprehension: An empirical study

Liu, Q., Mao, R., Geng, X., & Cambria, E. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103145 · Cited by 34

Highly Parallel Autoregressive Entity Linking with Discriminative Correction

Cao, N. D., Aziz, W. F., & Titov, I. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.604 · Cited by 44

Phrase Retrieval Learns Passage Retrieval, Too

Lee, J., Wettig, A., & Chen, D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.297 · Cited by 41

A Survey on Explainability in Machine Reading ComprehensionPreprint

Thayaparan, M., Valentino, M., & Freitas, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00389 · Cited by 54

BioReader: a Retrieval-Enhanced Text-to-Text Transformer for Biomedical Literature

Frisoni, G., Mizutani, M., Moro, G., & Valgimigli, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.390 · Cited by 29

Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering

Xu, W., Zhang, H., Cai, D., & Lam, W. P. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.90 · Cited by 35

HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions

Li, S., Li, X., Shang, L., Jiang, X., Liu, Q., Sun, C., Ji, Z., & Liu, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17568 · Cited by 26

Adaptive Information Seeking for Open-Domain Question Answering

Zhu, Y., Pang, L., Lan, Y., Shen, H., & Cheng, X. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.293 · Cited by 24

Dense Hierarchical Retrieval for Open-domain Question Answering

Liu, Y., Hashimoto, K., Zhou, Y., Yavuz, S., Xiong, C., & Yu, P. S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.19 · Cited by 41

EvidenceNet: Evidence Fusion Network for Fact Verification

Chen, Z., Hui, S. C., Zhuang, F., Liao, L., Li, F., Jia, M., & Li, J. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512135 · Cited by 24

Modeling Multi-hop Question Answering as Single Sequence Prediction

Yavuz, S., Hashimoto, K., Zhou, Y., Keskar, N. S., & Xiong, C. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.69 · Cited by 28

HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

Gutiérrez, B. J., Shu, Y., Gu, Y., Yasunaga, M., & Su, Y. (2024) · arXiv · doi:10.48550/arxiv.2405.14831 · Cited by 152

MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering

Liu, Q., Geng, X., Huang, H., Qin, T., Lü, J., & Jiang, D. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3107029 · Cited by 23

AFS Graph: Multidimensional Axiomatic Fuzzy Set Knowledge Graph for Open-Domain Question Answering

Lang, Q., Liu, X., & Jia, W. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3171677 · Cited by 24

XTQA: Span-Level Explanations for Textbook Question Answering

Ma, J., Chai, Q., Liu, J., Yin, Q., Wang, P., & Zheng, Q. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3294991 · Cited by 20

Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval

Zhao, C., Xiong, C., Boyd-Graber, J. L., & Daumé, H. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.368 · Cited by 25

Knowledge Enhanced Fact Checking and Verification

Zhu, B., Zhang, X., Gu, M., & Deng, Y. S. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3120636 · Cited by 22

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language InferencePreprint

Zhang, J., Hashimoto, K., Liu, W.-H., Wu, C.-S., Wan, Y., Yu, P. S., Socher, R., & Xiong, C. (2020) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.2010.13009 · Cited by 100

Cross-Thought for Sentence Encoder Pre-training

Wang, S., Fang, Y., Sun, S., Gan, Z., Cheng, Y., Liu, J., & Jiang, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.30 · Cited by 18

A Multi-Level Attention Model for Evidence-Based Fact Checking

Kruengkrai, C., Yamagishi, J., & Wang, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.217 · Cited by 20

Exploiting Reasoning Chains for Multi-hop Science Question Answering

Xu, W., Deng, Y., Zhang, H., Cai, D., & Lam, W. P. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.99 · Cited by 22

Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge

Ma, K., Cheng, H., Liu, X., Nyberg, E., & Gao, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.392 · Cited by 23

Chain-of-Skills: A Configurable Model for Open-Domain Question Answering

Ma, K., Cheng, H., Zhang, Y., Liu, X., Nyberg, E., & Gao, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.89 · Cited by 29

Generative Multi-hop Retrieval

Lee, H., Yang, S., Oh, H., & Seo, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.92 · Cited by 26

Asynchronous Multi-grained Graph Network For Interpretable Multi-hop Reading Comprehension

Li, R., Wang, L., Wang, S., & Jiang, Z. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/531 · Cited by 18

ELECTRA-based graph network model for multi-hop question answering

Zhu, P., Yuan, Y., & Chen, L. (2023) · Journal of Intelligent Information Systems · doi:10.1007/s10844-023-00800-5 · Cited by 15

Visual Explanation for Open-Domain Question Answering With BERT

Shao, Z., Sun, S., Zhao, Y., Wang, S., Wei, Z., Gui, T., Turkay, Ç., & Chen, S. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3243676 · Cited by 13

DC-BERT: Decoupling Question and Document for Efficient Contextual EncodingPreprint

Zhang, Y., Nie, P., Geng, X., Ramamurthy, A., Song, L., & Jiang, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12591 · Cited by 12

ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select

Zhuang, Y., Li, Y., Zhang, J., Yu, Y., Mou, Y., Chen, X., Song, L., & Zhang, C. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.46 · Cited by 12

Disentangled Retrieval and Reasoning for Implicit Question Answering

Liu, Q., Geng, X., Wang, Y., Cambria, E., & Jiang, D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3220933 · Cited by 11

Graph Reasoning with Context-Aware Linearization for Interpretable Fact Extraction and Verification

Kotonya, N., Spooner, T., Magazzeni, D., & Toni, F. (2021) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2021.fever-1.3 · Cited by 11

Answer Complex Questions: Path Ranker Is All You Need

Zhang, X., Zhan, K., Hu, E., Fu, C., Luo, L., Jiang, H., Jia, Y., Yu, F., Dou, Z., Cao, Z., et al. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462942 · Cited by 10

From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification

Zhang, H., Zhang, R., Guo, J., de Rijke, M., Fan, Y., & Cheng, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.422 · Cited by 10

Simple Compounded-Label Training for Fact Extraction and Verification

Nie, Y., Bauer, L., & Bansal, M. (2020) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2020.fever-1.1 · Cited by 10

Robustifying Multi-hop QA through Pseudo-Evidentiality Training

Lee, K., Hwang, S.-W., Han, S.-E., & Lee, D. (2021) · doi:10.18653/v1/2021.acl-long.476 · Cited by 9

Multi-level retrieval with semantic Axiomatic Fuzzy Set clustering for question answering

Lang, Q., Liu, X., & Deng, Y. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107858 · Cited by 10

Retrieve, Rerank, Read, then Iterate: Answering Open-Domain Questions of Arbitrary Complexity from Text.Preprint

Qi, P., Lee, H., Sido, O. T., & Manning, C. D. (2020) · arXiv (Cornell University) · Cited by 11

Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering

Sidiropoulos, G., Voskarides, N., Vakulenko, S., & Kanoulas, E. (2021) · Workshop on Simple and Efficient Natural Language Processing (SustaiNLP) · doi:10.18653/v1/2021.sustainlp-1.7 · Cited by 10

Triple-Fact Retriever: An explainable reasoning retrieval model for multi-hop QA problem

Wu, C., Hu, E., Zhan, K., Luo, L., Zhang, X., Jiang, H., Wang, Q., Cao, Z., Yu, F., & Chen, L. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00095 · Cited by 8

Few-shot Reranking for Multi-hop QA via Language Model Prompting

Khalifa, M., Logeswaran, L., Lee, M., Lee, H., & Wang, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.885 · Cited by 18

If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering

Yadav, V., Bethard, S. J., & Surdeanu, M. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.363 · Cited by 9

Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

(2021) · doi:10.18653/v1/2021.nlp4if-1 · Cited by 8

A syntactic evidence network model for fact verification

Chen, Z., Hui, S. C., Zhuang, F., Liao, L., Jia, M., Li, J., & Huang, H. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106424 · Cited by 8

Different paths to the same destination: Diversifying LLMs generation for multi-hop open-domain question answering

Li, R., Wang, Y., Wen, Z., Cui, M., & Miao, Q. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112789 · Cited by 10

Coarse and Fine Granularity Graph Reasoning for Interpretable Multi-Hop Question Answering

Zhang, M., Li, F., Wang, Y., Zhang, Z., Zhou, Y., & Li, X. (2020) · IEEE Access · doi:10.1109/access.2020.2981134 · Cited by 7

Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)

(2020) · doi:10.18653/v1/2020.fever-1 · Cited by 8

CosG: A Graph-Based Contrastive Learning Method for Fact Verification

Chen, C., Zheng, J., & Chen, H. (2021) · Sensors · doi:10.3390/s21103471 · Cited by 7

Mutually improved dense retriever and GNN-based reader for arbitrary-hop open-domain question answering

Li, R., Wang, L., Jiang, Z., Hu, Z., Zhao, M., & Lu, X. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07072-0 · Cited by 6

Multi-Step Reasoning Over Unstructured Text with Beam Dense RetrievalPreprint

Zhao, C., Xiong, C., Boyd-Graber, J. L., & Daumé, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.05883 · Cited by 6

Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization

Ni, A., Gardner, M., & Dasigi, P. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.497 · Cited by 11

Multi-Hop Fact Checking of Political Claims

Atanasova, P. (2022) · Springer eBooks · doi:10.1007/978-3-031-51518-7_6 · Cited by 5

Weakly Supervised Pre-Training for Multi-Hop Retriever

Seonwoo, Y., Lee, S.-W., Kim, J., Ha, J.-W., & Oh, A. H. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.62 · Cited by 5

Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning

Guo, W., Gong, Q., Rao, Y., & Lai, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.231 · Cited by 5

Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games

Feng, Y., Yu, M., Xiong, W., Guo, X., Huang, J., Chang, S., Campbell, M. S., Greenspan, M., & Zhu, X. (2021) · doi:10.21428/594757db.ce391ef3 · Cited by 6

Evidence-Based Document-Level Event Factuality Identification

Zhang, H., Zhong, Q., Li, P., & Zhu, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20865-2_18 · Cited by 4

Simple and Efficient ways to Improve REALM

Balachandran, V., Vaswani, A., Tsvetkov, Y., & Parmar, N. (2021) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/2021.mrqa-1.16 · Cited by 5

Generating Followup Questions for Interpretable Multi-hop Question AnsweringPreprint

Malon, C. D., & Bai, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12344 · Cited by 11

DDRQA: Dynamic Document Reranking for Open-domain Multi-hop Question Answering.Preprint

Zhang, Y., Nie, P., Ramamurthy, A., & Song, L. (2020) · arXiv (Cornell University) · Cited by 4

Concept-enhanced heterogeneous graph network for fact verification

Chen, Z., Liao, L., Hui, S. C., & Huang, H. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107959 · Cited by 3

The “Curious Case of Contexts” in Retrieval‐Augmented Generation With a Combination of Labeled and Unlabeled Data

Santra, P., Ghosh, M., Ganguly, D., Basuchowdhuri, P., & Naskar, S. K. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70021 · Cited by 5

Effectively Modeling Sentence Interactions With Factorization Machines for Fact Verification

Chen, Z., Zhuang, F., Liao, L., Jia, M., Li, J., & Huang, H. (2023) · IEEE Intelligent Systems · doi:10.1109/mis.2023.3301170 · Cited by 4

Fact-checking: relevance assessment of references in the Polish political domain

Sawczyn, A., Binkowski, J., Janiak, D., Augustyniak, Ł., & Kajdanowicz, T. J. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.08.132 · Cited by 3

Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking

Bekoulis, G., Papagiannopoulou, C., & Deligiannis, N. (2021) · Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda (NLP4IF) · doi:10.18653/v1/2021.nlp4if-1.4 · Cited by 3

Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)

(2022) · doi:10.18653/v1/2022.suki-1 · Cited by 3

Hierarchical Representations in Dense Passage Retrieval for Question-Answering

Ennen, P., Freddi, F., Lin, C.-J., Kung, P.-N., Wang, R., Yang, C., Shiu, D.-S., & Bernacchia, A. (2023) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2023.fever-1.2 · Cited by 2

LogiLaw Dataset Towards Reinforcement Learning from Logical Feedback (RLLF)

Nguyen, H.-T., Fungwacharakorn, W., & Satoh, K. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230967 · Cited by 4

Investigating Zero- and Few-shot Generalization in Fact Verification

Pan, L., Zhang, Y., & Kan, M. (2023) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2023.ijcnlp-main.34 · Cited by 7

Using the Hammer only on Nails: A Hybrid Method for Representation-Based Evidence Retrieval for Question Answering

Liang, Z., Zhao, Y., & Surdeanu, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72113-8_22 · Cited by 3

Complementary Evidence Identification in Open-Domain Question Answering

Mou, X., Yu, M., Chang, S., Feng, Y., Zhang, L., & Su, H. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.234 · Cited by 1

Knowledge Transfer between Structured and Unstructured Sources for Complex Question Answering

Mo, L., Wang, Z., Zhao, J., & Sun, H. (2022) · doi:10.18653/v1/2022.suki-1.7 · Cited by 2

Adaptive Hyper-parameter Learning for Deep Semantic Retrieval

Li, M., Yuan, C., Wang, H., Wang, P., Zhuo, J., Wang, B., Liu, L., & Xu, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.72 · Cited by 3

Improving Machine Reading Comprehension with General Reading Strategies

Sun, K., Yu, D., Yu, D., & Cardie, C. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1270 · Cited by 134

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Huang, L., Bras, R. J. L., Bhagavatula, C., & Choi, Y. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1243 · Cited by 302

Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading

Qin, L., Galley, M., Brockett, C., Liu, X., Gao, X., Dolan, B., Choi, Y., & Gao, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1539 · Cited by 100

Attention-aware semantic relevance predicting Chinese sentence reading

Sun, K. (2025) · Universitätsbibliothek Tübingen · doi:10.15496/publikation-110952