{"list":"other","total":78,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1301.3781","title":"Efficient Estimation of Word Representations in Vector Space","created":"2013-01-16","cited_by_count":4375},{"id":"1909.10351","title":"TinyBERT: Distilling BERT for Natural Language Understanding","created":"2019-09-23","cited_by_count":1167},{"id":"1809.08887","title":"Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task","created":"2018-09-24","cited_by_count":1139},{"id":"1906.04341","title":"What Does BERT Look At? An Analysis of BERT's Attention","created":"2019-06-11","cited_by_count":1003},{"id":"1607.01759","title":"Bag of Tricks for Efficient Text Classification","created":"2016-07-06","cited_by_count":876},{"id":"2002.12327","title":"A Primer in BERTology: What we know about how BERT works","created":"2020-02-27","cited_by_count":815},{"id":"2009.06732","title":"Efficient Transformers: A Survey","created":"2020-09-14","cited_by_count":790},{"id":"1709.00103","title":"Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning","created":"2017-08-31","cited_by_count":687},{"id":"1908.07442","title":"TabNet: Attentive Interpretable Tabular Learning","created":"2019-08-20","cited_by_count":648},{"id":"2109.00122","title":"FinQA: A Dataset of Numerical Reasoning over Financial Data","created":"2021-09-01","cited_by_count":562},{"id":"1912.13318","title":"LayoutLM: Pre-training of Text and Layout for Document Image Understanding","created":"2019-12-31","cited_by_count":553},{"id":"1508.00305","title":"Compositional Semantic Parsing on Semi-Structured Tables","created":"2015-08-03","cited_by_count":515},{"id":"2004.02349","title":"TAPAS: Weakly Supervised Table Parsing via Pre-training","created":"2020-04-05","cited_by_count":434},{"id":"1909.02164","title":"TabFact: A Large-scale Dataset for Table-based Fact Verification","created":"2019-09-05","cited_by_count":428},{"id":"2012.06678","title":"TabTransformer: Tabular Data Modeling Using Contextual Embeddings","created":"2020-12-11","cited_by_count":409},{"id":"2105.07624","title":"TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance","created":"2021-05-17","cited_by_count":408},{"id":"2005.08314","title":"TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data","created":"2020-05-17","cited_by_count":398},{"id":"1611.01368","title":"Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies","created":"2016-11-04","cited_by_count":354},{"id":"1908.07836","title":"PubLayNet: largest dataset ever for document layout analysis","created":"2019-08-16","cited_by_count":351},{"id":"2106.01342","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","created":"2021-06-02","cited_by_count":319},{"id":"1906.05714","title":"A Multiscale Visualization of Attention in the Transformer Model","created":"2019-06-12","cited_by_count":292},{"id":"2004.14373","title":"ToTTo: A Controlled Table-To-Text Generation Dataset","created":"2020-04-29","cited_by_count":268},{"id":"2004.07347","title":"HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data","created":"2020-04-15","cited_by_count":264},{"id":"2106.11539","title":"DocFormer: End-to-End Transformer for Document Understanding","created":"2021-06-22","cited_by_count":256},{"id":"2107.07653","title":"TAPEX: Table Pre-training via Learning a Neural SQL Executor","created":"2021-07-16","cited_by_count":235},{"id":"2201.05966","title":"UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models","created":"2022-01-16","cited_by_count":231},{"id":"1707.08052","title":"Challenges in Data-to-Document Generation","created":"2017-07-25","cited_by_count":226},{"id":"1608.04207","title":"Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks","created":"2016-08-15","cited_by_count":216},{"id":"1806.03384","title":"Data Synthesis based on Generative Adversarial Networks","created":"2018-06-09","cited_by_count":185},{"id":"2009.13845","title":"GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing","created":"2020-09-29","cited_by_count":171},{"id":"1704.03471","title":"What do Neural Machine Translation Models Learn about Morphology?","created":"2017-04-11","cited_by_count":166},{"id":"2005.00589","title":"Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context","created":"2020-05-01","cited_by_count":147},{"id":"2012.12627","title":"Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing","created":"2020-12-23","cited_by_count":133},{"id":"2010.12773","title":"Structure-Grounded Pretraining for Text-to-SQL","created":"2020-10-24","cited_by_count":126},{"id":"2108.06712","title":"HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation","created":"2021-08-15","cited_by_count":118},{"id":"2105.02584","title":"TABBIE: Pretrained Representations of Tabular Data","created":"2021-05-06","cited_by_count":111},{"id":"2005.10433","title":"Text-to-Text Pre-Training for Data-to-Text Tasks","created":"2020-05-21","cited_by_count":104},{"id":"1903.01949","title":"TableBank: A Benchmark Dataset for Table Detection and Recognition","created":"2019-03-05","cited_by_count":102},{"id":"2010.12537","title":"TUTA: Tree-based Transformers for Generally Structured Table Pre-training","created":"2020-10-21","cited_by_count":100},{"id":"1908.04729","title":"Complicated Table Structure Recognition","created":"2019-08-13","cited_by_count":92},{"id":"2004.10404","title":"Logical Natural Language Generation from Open-Domain Tables","created":"2020-04-22","cited_by_count":90},{"id":"2103.13136","title":"Representing Numbers in NLP: a Survey and a Vision","created":"2021-03-24","cited_by_count":83},{"id":"2010.00571","title":"Understanding tables with intermediate pre-training","created":"2020-10-01","cited_by_count":68},{"id":"2012.10309","title":"Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training","created":"2020-12-18","cited_by_count":65},{"id":"2109.04312","title":"MATE: Multi-view Attention for Table Transformer Efficiency","created":"2021-09-09","cited_by_count":54},{"id":"2002.08087","title":"LAMBERT: Layout-Aware (Language) Modeling for information extraction","created":"2020-02-19","cited_by_count":53},{"id":"2004.14356","title":"AxCell: Automatic Extraction of Results from Machine Learning Papers","created":"2020-04-29","cited_by_count":48},{"id":"2004.14579","title":"Logic2Text: High-Fidelity Natural Language Generation from Logical Forms","created":"2020-04-30","cited_by_count":45},{"id":"2106.07258","title":"GitTables: A Large-Scale Corpus of Relational Tables","created":"2021-06-14","cited_by_count":45},{"id":"2110.08518","title":"MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding","created":"2021-10-16","cited_by_count":45},{"id":"2106.15339","title":"SpreadsheetCoder: Formula Prediction from Semi-structured Context","created":"2021-06-26","cited_by_count":41},{"id":"2107.07261","title":"Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills","created":"2021-07-15","cited_by_count":39},{"id":"1808.04819","title":"VizML: A Machine Learning Approach to Visualization Recommendation","created":"2018-08-14","cited_by_count":38},{"id":"1906.00041","title":"Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval","created":"2019-05-31","cited_by_count":37},{"id":"2106.03096","title":"TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data","created":"2021-06-06","cited_by_count":36},{"id":"2012.02469","title":"RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation","created":"2020-12-04","cited_by_count":27},{"id":"2102.09460","title":"TCN: Table Convolutional Network for Web Table Interpretation","created":"2021-02-17","cited_by_count":24},{"id":"2105.01736","title":"Retrieving Complex Tables with Multi-Granular Graph Representation Learning","created":"2021-05-04","cited_by_count":24},{"id":"1802.06159","title":"Ad Hoc Table Retrieval using Semantic Similarity","created":"2018-02-16","cited_by_count":21},{"id":"2101.10573","title":"Representations for Question Answering from Documents with Tables and Text","created":"2021-01-26","cited_by_count":21},{"id":"1906.00781","title":"Learning Semantic Annotations for Tabular Data","created":"2019-05-30","cited_by_count":17},{"id":"2008.11015","title":"Table2Charts: Recommending Charts by Learning Shared Table Representations","created":"2020-08-24","cited_by_count":14},{"id":"2104.08296","title":"Learning to Reason for Text Generation from Scientific Tables","created":"2021-04-16","cited_by_count":14},{"id":"2106.13500","title":"TableSense: Spreadsheet Table Detection with Convolutional Neural Networks","created":"2021-06-25","cited_by_count":12},{"id":"1608.00339","title":"Crowd-sourcing NLG Data: Pictures Elicit Better Data","created":"2016-08-01","cited_by_count":11},{"id":"1708.08721","title":"EntiTables: Smart Assistance for Entity-Focused Tables","created":"2017-08-29","cited_by_count":11},{"id":"1902.01740","title":"TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables","created":"2019-02-05","cited_by_count":9},{"id":"1706.02427","title":"Content-Based Table Retrieval for Web Queries","created":"2017-06-08","cited_by_count":8},{"id":"1802.06290","title":"TabVec: Table Vectors for Classification of Web Tables","created":"2018-02-17","cited_by_count":8},{"id":"2106.00479","title":"DoT: An efficient Double Transformer for NLP tasks with tables","created":"2021-06-01","cited_by_count":8},{"id":"2010.06801","title":"A Graph Representation of Semi-structured Data for Web Question Answering","created":"2020-10-14","cited_by_count":7}],"next_cursor":null}