TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648

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

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515

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

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687

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

Gaussian Error Linear Units (GELUs)

Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1606.08415 · Cited by 3,537

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00300 · Cited by 1,827

Unlock the Potential of Large Language Models for Predictive Tabular Tasks in Data Science With Table-Specific Pretraining

Yang, Y., WANG, Y. Y., Li, Y., Sen, S., Li, L., Qiu, L., & Liu, Q. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3673401 · Cited by 16

Weakly Supervised Semantic Parsing by Learning from Mistakes

Guo, J., Lou, J., Liu, T., & Zhang, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.222 · Cited by 6

HoogBERTa: Multi-task Sequence Labeling using Thai Pretrained Language Representation

Porkaew, P., Boonkwan, P., & Supnithi, T. (2021) · International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) · doi:10.1109/isai-nlp54397.2021.9678190 · Cited by 3

Schema dependency-enhanced curriculum pre-training for table semantic parsing

Qin, B., Hui, B., Wang, L., Yang, M., Li, B., Huang, F., Si, L., Jiang, Q., & Li, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110264 · Cited by 3

GetPt: Graph-enhanced General Table Pre-training with Alternate Attention Network

Jia, R., Guo, H., Jin, X., Yan, C., Du, L., Ma, X., Stankovic, T., Lozajic, M., Zoranovic, G., Ilic, I., et al. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599366 · Cited by 3

Improving compositional generalization for multi-step quantitative reasoning in question answering

Nourbakhsh, A., Jiao, C., Shah, S., & Rosé, C. P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.125 · Cited by 3

Building the classification model based on the genetic algorithm and the improved Bayesian method

Phamtoan, D., & Vovan, T. (2023) · International Journal of Data Science and Analytics · doi:10.1007/s41060-023-00436-2 · Cited by 3

TTC-QuAli: A Text-Table-Chart Dataset for Multimodal Quantity Alignment

Dong, H., Wang, H., Zhou, A., & Hu, Y. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635777 · Cited by 3

Auto-Tables: Relationalize Tables without Using Examples

Li, P., He, Y., Yan, C., Wang, Y., & Chaudhuri, S. (2024) · ACM SIGMOD Record · doi:10.1145/3665252.3665269 · Cited by 3

Analysis of the Effectiveness of Model, Data, and User-Centric Approaches for Chat Application: A Case Study of BlenderBot 2.0

Park, C., Lee, J., Son, S., Park, K., Jang, J., & Lim, H. (2024) · Applied Sciences · doi:10.3390/app14114821 · Cited by 3

Domain-Specific Few-Shot Table Prompt Question Answering via Contrastive Exemplar Selection

Mo, T., Xiao, Q., Zhang, H., Li, R., & Wu, Y. (2024) · Algorithms · doi:10.3390/a17070278 · Cited by 3

TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations

Nobari, A. D., & Rafiei, D. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3749646.3749657 · Cited by 3

Quantify production planning efficiency through predictive modeling in manufacturing systems

Monaco, S., Apiletti, D., Francica, A., & Cerquitelli, T. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.110919 · Cited by 3

Large language model vs. traditional machine learning: Evaluating predictive models for early detection of tumor relapse

Timilsina, M., Buosi, S., Torrente, M., Provencio, M., Cobo, M., Rodríguez‐Abreu, D., Castro, R. L., Carcereny, E., Curry, E., & Nováček, V. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127641 · Cited by 3

LLaSA: Large Language and Structured Data Assistant

Xu, Y., He, S., Chen, J., ZengXiangrong, Z., Wang, B., Liu, G., Zhao, J., & Liu, K. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.97 · Cited by 3

XInfoTabS: Evaluating Multilingual Tabular Natural Language Inference

Minhas, B., Shankhdhar, A., Gupta, V., Aggarwal, D., & Zhang, S. (2022) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2022.fever-1.7 · Cited by 3

Towards Explainable Table Interpretation Using Multi-view Explanations

Gao, Y., Wang, P., Zeng, X., Chen, L., Mao, Y., Wei, Z., & Li, M. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00094 · Cited by 3

Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations

Chen, J., Zhang, Y., Liu, L., Dong, R., Chen, X., Ng, P., Wang, W. Y., & Huang, Z. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.341 · Cited by 5

Named Entity Recognition in Industrial Tables using Tabular Language Models

Koleva, A., Ringsquandl, M., Buckley, M., Hasan, R., & Tresp, V. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-industry.35 · Cited by 3

Leveraging Data Recasting to Enhance Tabular Reasoning

Jena, A., Gupta, V., Shrivastava, M., & Eisenschlos, J. M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.328 · Cited by 3

Semantic Annotation of Russian-Language Tables Based on a Pre-Trained Language Model

Tobola, K. V., & Dorodnykh, N. O. (2024) · Ivannikov Memorial Workshop (IVMEM) · doi:10.1109/ivmem63006.2024.10659709 · Cited by 3

RePanda: Pandas-powered Tabular Verification and Reasoning

Chegini, A., Rezaei, K., Eghbal-zadeh, H., & Feizi, S. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1549 · Cited by 6

Automatic Numerical Question Answering on Table using BERT-GNN

Bagwe, R., & George, K. (2020) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon51285.2020.9298028 · Cited by 3

Hydra: A System for Large Multi-Model Deep LearningPreprint

Nagrecha, K., & Kumar, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.08633 · Cited by 5

Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)

(2022) · doi:10.18653/v1/2022.fever-1 · 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

Dependency-Aware Core Column Discovery for Table Understanding

Qiu, J., Song, A., Jin, J., Zhang, T., Ding, J., Fang, X., & Qian, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47240-4_9 · Cited by 2

KU-DMIS at EHRSQL 2024 : Generating SQL query via question templatization in EHR

Kim, H., Kim, C., Lee, H., Jang, K., Lee, J., Lee, K., Kim, G., & Kang, J. (2024) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2024.clinicalnlp-1.64 · Cited by 2

TabMoE: A General Framework for Diverse Table-Based Reasoning with Mixture-of-Experts

Wu, J., & Hou, M. (2024) · Mathematics · doi:10.3390/math12193031 · Cited by 3

Enhancing Free-Form Table Question Answering Models by Distilling Relevant-Cell-Based Rationales

Yang, Z., Wang, S., Yan, Y., Liu, P., & Yu, D. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8367-0_1 · Cited by 2

TabVer: Tabular Fact Verification with Natural Logic

Aly, R., & Vlachos, A. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00722 · Cited by 3

Evaluating Fairness and Bias in Large Language Models for Tabular Data

Tayebi, A., & Garibay, Ö. Ö. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93415-5_7 · Cited by 2

Retrieving Tables via Inter- and Intra-Content Contrastive Representation Learning

Wu, J., & Hou, M. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730245 · Cited by 2

A Survey of NL2SQL Research: From Foundations to Frontiers

Zhao, P., Shao, M., Cui, J., & Zhou, H. (2025) · International Conference on Intelligent Perception and Pattern Recognition (IPPR) · doi:10.1109/ippr66507.2025.11198387 · Cited by 2

TableTale: Reviving the Narrative Interplay Between Data Tables and Text in Scientific Papers

Wang, L., Zhang, Z., Cao, Y., Tsung, F., & Luo, Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791534 · Cited by 2

Beyond Pure Text: Summarizing Financial Reports Based on Both Textual and Tabular Data

Wang, Z., Jiang, Z., Zhang, X., Soon, J., Zhang, J., Xiaoyao, W., & Du, H. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/581 · Cited by 2

A Table Question Alignment based Cell-Selection Method for Table-Text QA

Wu, J., Xu, Y., Karlsson, B. F., & Okumura, M. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.189 · Cited by 2

Multi-Paragraph Machine Reading Comprehension with Hybrid Reader over Tables and Text

Cho, S., Park, S., Kim, H.-L., Lee, J., Shin, J., & Kwon, H. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2367820 · Cited by 3

Row-based hierarchical graph network for multi-hop question answering over textual and tabular data

Yang, P., Li, W., Zhao, G., & Zha, X. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-022-05035-9 · Cited by 2

InfoSync: Information Synchronization across Multilingual Semi-structured Tables

Khincha, S., Jain, C., Gupta, V., Kataria, T., & Zhang, S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.159 · Cited by 2

Program Synthesis for Complex QA on Charts via Probabilistic Grammar Based Filtered Iterative Back-Translation

Bhaisaheb, S., Paliwal, S. S., Patil, R., Patwardhan, M. S., Vig, L., & Shroff, G. M. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.189 · Cited by 2

Enhancing Structured Evidence Extraction for Fact Verification

Wu, Z., Hu, N., & Feng, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.409 · Cited by 2

Wiki-TabNER: Integrating Named Entity Recognition into Wikipedia Tables

Koleva, A., Ringsquandl, M., Hatem, A., Runkler, T. A., & Volker Tresp, C. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730344 · Cited by 2

Graph Neural Network Approach to Semantic Type Detection in Tables

Hoseinzade, E., & Wang, K. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-2266-2_10 · Cited by 2

A Hierarchical Multi-Task Learning Framework for Semantic Annotation in Tabular Data

Wu, J., & Hou, M. (2024) · Entropy · doi:10.3390/e26080664 · Cited by 2

Exploring Downscaling in High-Dimensional Lorenz Models Using the Transformer Decoder

Shen, B. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6040107 · Cited by 2

On the Use of Large Language Models for Table Tasks

Dong, Y., Oyamada, M., Xiao, C., & Zhang, H. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679100 · Cited by 3

The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers

Deng, X., Wang, X., & Stevenson, M. (2025) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3731120.3744614 · Cited by 5

Tabular Functional Block Detection with Embedding-based Agglomerative Cell Clustering

Sun, K., Wang, F., Chen, M., & Pujara, J. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482484 · Cited by 2

HiddenTables and PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies

Watson, W., Cho, N., Balch, T. R., & Veloso, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.442 · Cited by 5

Realistic Data Augmentation Framework for Enhancing Tabular Reasoning

Kumar, D., Gupta, V., Sharma, S., & Zhang, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.324 · Cited by 2

Talk2Data: High-Level Question Decomposition for Data-Oriented Question and AnsweringPreprint

Shi, D., Guo, Y., Guo, M., Wu, Y., Chen, Q., & Cao, N. (2021) · arXiv (Cornell University) · Cited by 2

Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question AnsweringPreprint

Li, A. H., Ng, P., Xu, P., Zhu, H., Wang, Z., & Xiang, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02866 · Cited by 4

Making Table Understanding Work in PracticePreprint

Hulsebos, M., Gathani, S., Gale, J., Dillig, I., Groth, P., & Demiralp, Ç. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05173 · Cited by 2

Tabular Deep Learning: A Survey from Small Neural Networks to Large Language ModelsPreprint

Raieli, S. (2025) · doi:10.36227/techrxiv.175753732.26052568/v1 · Cited by 2

MPFToD: a modularized pre-training framework for consistency identification in task-oriented dialogue

Qin, L., Huang, S., Chen, Q., Liu, Q., Che, W., & Xu, R. (2025) · Frontiers of Computer Science · doi:10.1007/s11704-024-3778-9 · Cited by 1

A Table Classification Approach Utilizing Graph Attention Network

Tian, Z., Rao, W., Tang, Z., & Jiang, T. (2025) · International Conference on Digital Image Processing and Computer Applications (DIPCA) · doi:10.1109/dipca65051.2025.11042300 · Cited by 1

HAETAE: In-domain Table Pretraining with Header Anchoring

Jung, W., & Yoon, S. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730205 · Cited by 1

Converting Human Text into Database Queries Using Natural Language Processing: A Hybrid Approach with Multinomial Logistic Regression and T5

Aphale, A., Chavan, S., Jadhav, N., Balsurkar, P., Mali, P. R., & Bhagat, D. A. (2025) · International Conference on Emerging Trends in Engineering and Technology (ICETET) · doi:10.1109/icetetsip64213.2025.11156919 · Cited by 1

Tab-HGNN: Learning Column Representation with Heterogeneous Graph Neural Network for Web Table Interpretation

Su, X., & Shen, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020362 · Cited by 1

Semantic Annotation of Relational Schemas Using a Probabilistic Generative Model

Mukherjee, D., Bandyopadhyay, A., Datta, S., & Bhattacharya, I. (2024) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3632410.3632414 · Cited by 1

A Survey of Mixture Models for Scientific Literature

Deng, Y., Liu, Q., Li, X., Zhang, G., & Zhu, M. Q. (2024) · International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM) · doi:10.1109/aiim64537.2024.10934484 · Cited by 1

Efficient Multi-Expert Tabular Language Model for Banking

Guo, Y., Zhang, W., Zhang, X., Zheng, V. W., & Yang, Y. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709400 · Cited by 3

A Table Semantic Retrieval Method Using Self-supervised Pre-training Querying Transformer and Text-Attributed Graphs

Shen, Y., Ye, L., & Xie, Z. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9986-5_1 · Cited by 1

Panel on Neural Relational Data: Tabular Foundation Models, LLMs... or Both?

Papotti, P., & Binnig, C. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3750601.3760519 · Cited by 1

Arabic WikiTableQA: Benchmarking Question Answering over Arabic Tables Using Large Language Models

Alsolami, F. J., & Alrayzah, A. (2025) · Electronics · doi:10.3390/electronics14193829 · Cited by 1

Bridging Queries and Tables through Entities in Open-Domain Table Retrieval

Li, D., Bi, K., Guo, J., & Cheng, X. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761002 · Cited by 4

An Empirical study on LLM-based Log Retrieval for Software Engineering Metadata Management

Sun, S., Jin, Y., & Staron, M. (2025) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3756681.3756989 · Cited by 1

TabEmb: Joint Semantic-Structure Embedding for Table Annotation

Hoseinzade, E., Wang, K., & Raju, A. D. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.757 · Cited by 1

FedAugment: Table Augmentation Search over Decentralized Data Repositories

Behme, L., Badura, E., Geißler, L., Böehm, M., Abedjan, Z., & Markl, V. (2026) · Proceedings of the VLDB Endowment · doi:10.14778/3828612.3828623 · Cited by 1

A Question Answering System for Unstructured Table Images

Xue, W., Cai, S., Wang, W., Li, Q., Yu, B., Zhan, Y., & Tao, D. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3478558 · Cited by 1

COMBINE: A Pipeline for SQL Generation from Natural Language

Mellah, Y., Rhouati, A., Ettifouri, E. H., Bouchentouf, T., & Belkasmi, M. G. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-88244-0_10 · Cited by 2

Graph Alignment for Cross-Domain Text-to-SQL

Liu, Y., Hu, Y.-H., Li, Z., & Zhu, Z. (2022) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp54964.2022.9778427 · Cited by 1

A survey of deep learning based text-to-SQL generation

梁, 清., 朱, 琪., 孙, 泽., 张, 路., 张, 文., 熊, 英., 梁, 广., & 郁, 莲. (2022) · Scientia Sinica Informationis · doi:10.1360/ssi-2021-0316 · Cited by 1

Transformers, Tables and Frame Semantics

Ramirez, M., Bogatu, A., Paton, N. W., & Freitas, A. (2023) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc56153.2023.00033 · Cited by 1

CLPL: A Self-supervised Contrastive Learning Pseudo-Labeling Framework for Tabular Data

Farahani, A., Tonekaboni, N. H., Rasheed, K., & Arabnia, H. R. (2022) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci58124.2022.00009 · Cited by 2

Enhancing Table Retrieval with Dual Graph Representations

Liu, T., Zhang, X., Zhang, Z., Wang, Y., Li, Q., Zhang, S., & Liu, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43421-1_7 · Cited by 1

A Multi-Task Learning Framework for Reading Comprehension of Scientific Tabular Data

Xu, Y., Zhang, M., Fan, J., Luo, Z., & Yang, Y. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00285 · Cited by 1

A Transformer Architecture for Risk Analysis of Group Effects of Food Nutrients

Balandina, A., Gruzdev, B. V., Savelev, N. A., Budakyan, Y. S., Kisil, S., Bogdanov, A. R., & Grachev, E. A. (2024) · Moscow University Physics Bulletin · doi:10.3103/s0027134924702291 · Cited by 2

Geodesic Flow Kernels for Semi-Supervised Learning on Mixed-Variable Tabular Dataset

Hwang, Y., & Lee, Y. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i17.33928 · Cited by 1

Table Overlap Estimation through Graph Embeddings

Pugnaloni, F., Zecchini, L., Paganelli, M., Lissandrini, M., Naumann, F., & Simonini, G. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725365 · Cited by 1

A Deep Learning Approach for Event-Driven Duplicate Detection

Stippel, C., Pratellesi, C., Schwendinger, B., & Hoch, R. (2025) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict64582.2025.00030 · Cited by 1

LLM-Symbolic Integration for Robust Temporal Tabular Reasoning

Kulkarni, A., Dixit, K., Srikumar, V., Roth, D., & Gupta, V. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.1022 · Cited by 1

Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with Tables

Ruan, X., Jin, M., Ma, J., Yang, H., Jiang, L., Mo, Y., & Zhou, M. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.179 · Cited by 1

Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model

Negreanu, C., Karaoglu, A., Williams, J. M., Chen, S., Fabian, D., Gordon, A., & Lin, C.-Y. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524923 · Cited by 1

Bilingual Tabular Inference: A Case Study on Indic Languages

Agarwal, C., Gupta, V., Kunchukuttan, A., & Shrivastava, M. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.295 · Cited by 1

Neural Network Accelerated Tuple Search For Relational Data

Ma, L., & Pu, K. Q. (2022) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri54793.2022.00029 · Cited by 1

Progressive Feature Upgrade in Semi-supervised Learning on Tabular Domain

Gharasuie, M. M., & Wang, F. (2022) · IEEE International Conference on Knowledge Graph (ICKG) · doi:10.1109/ickg55886.2022.00031 · Cited by 1

SheetPT: Spreadsheet Pre-training Based on Hierarchical Attention Network

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Towards deeply intelligent interfaces in relational databases

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