{"list":"other","total":53,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"1908.10084","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","created":"2019-08-27","cited_by_count":9170},{"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":"1702.08734","title":"Billion-scale similarity search with GPUs","created":"2017-02-28","cited_by_count":2497},{"id":"2104.08821","title":"SimCSE: Simple Contrastive Learning of Sentence Embeddings","created":"2021-04-18","cited_by_count":2173},{"id":"1905.07129","title":"ERNIE: Enhanced Language Representation with Informative Entities","created":"2019-05-17","cited_by_count":528},{"id":"1911.06136","title":"KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation","created":"2019-11-13","cited_by_count":356},{"id":"2010.01057","title":"LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention","created":"2020-10-02","cited_by_count":326},{"id":"2002.01808","title":"K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters","created":"2020-02-05","cited_by_count":319},{"id":"1909.04164","title":"Knowledge Enhanced Contextual Word Representations","created":"2019-09-09","cited_by_count":296},{"id":"1810.10147","title":"FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation","created":"2018-10-24","cited_by_count":254},{"id":"2103.08493","title":"How Many Data Points is a Prompt Worth?","created":"2021-03-15","cited_by_count":198},{"id":"2104.07650","title":"KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction","created":"2021-04-15","cited_by_count":180},{"id":"1601.00770","title":"End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures","created":"2016-01-05","cited_by_count":161},{"id":"2303.08559","title":"Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!","created":"2023-03-15","cited_by_count":119},{"id":"1910.11470","title":"A Survey on Recent Advances in Named Entity Recognition from Deep Learning models","created":"2019-10-25","cited_by_count":117},{"id":"1706.05075","title":"Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme","created":"2017-06-07","cited_by_count":102},{"id":"2305.05003","title":"Revisiting Relation Extraction in the era of Large Language Models","created":"2023-05-08","cited_by_count":102},{"id":"2004.14855","title":"TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task","created":"2020-04-30","cited_by_count":96},{"id":"1905.08284","title":"Enriching Pre-trained Language Model with Entity Information for Relation Classification","created":"2019-05-20","cited_by_count":82},{"id":"2102.01373","title":"An Improved Baseline for Sentence-level Relation Extraction","created":"2021-02-02","cited_by_count":81},{"id":"2312.02296","title":"LLMs Accelerate Annotation for Medical Information Extraction","created":"2023-12-04","cited_by_count":77},{"id":"2010.01923","title":"Learning from Context or Names? An Empirical Study on Neural Relation Extraction","created":"2020-10-05","cited_by_count":75},{"id":"2104.08398","title":"Re-TACRED: Addressing Shortcomings of the TACRED Dataset","created":"2021-04-16","cited_by_count":74},{"id":"1911.09886","title":"Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction","created":"2019-11-22","cited_by_count":73},{"id":"2010.13415","title":"TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking","created":"2020-10-26","cited_by_count":72},{"id":"2305.02105","title":"GPT-RE: In-context Learning for Relation Extraction using Large Language Models","created":"2023-05-03","cited_by_count":62},{"id":"2205.12696","title":"Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction","created":"2022-05-25","cited_by_count":57},{"id":"2011.01675","title":"Joint Entity and Relation Extraction with Set Prediction Networks","created":"2020-11-03","cited_by_count":54},{"id":"2004.03186","title":"More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction","created":"2020-04-07","cited_by_count":52},{"id":"1812.04361","title":"RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information","created":"2018-12-11","cited_by_count":47},{"id":"1808.08013","title":"Reinforcement Learning for Relation Classification from Noisy Data","created":"2018-08-24","cited_by_count":46},{"id":"1712.05191","title":"Relation Extraction : A Survey","created":"2017-12-14","cited_by_count":44},{"id":"1904.01246","title":"UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering","created":"2019-04-02","cited_by_count":37},{"id":"2305.01555","title":"How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?","created":"2023-05-02","cited_by_count":30},{"id":"2306.02051","title":"A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers","created":"2023-06-03","cited_by_count":22},{"id":"2210.09345","title":"CrossRE: A Cross-Domain Dataset for Relation Extraction","created":"2022-10-17","cited_by_count":21},{"id":"2004.08134","title":"Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction","created":"2020-04-17","cited_by_count":18},{"id":"2204.13516","title":"What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification","created":"2022-04-28","cited_by_count":17},{"id":"2305.18322","title":"REFinD: Relation Extraction Financial Dataset","created":"2023-05-22","cited_by_count":17},{"id":"1605.07895","title":"Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey","created":"2016-05-25","cited_by_count":16},{"id":"1705.03645","title":"A Survey of Deep Learning Methods for Relation Extraction","created":"2017-05-10","cited_by_count":16},{"id":"2211.09039","title":"UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction","created":"2022-11-16","cited_by_count":14},{"id":"1901.08163","title":"Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing","created":"2019-01-23","cited_by_count":13},{"id":"2210.12435","title":"Generative Prompt Tuning for Relation Classification","created":"2022-10-22","cited_by_count":13},{"id":"2101.01907","title":"Deep Neural Network Based Relation Extraction: An Overview","created":"2021-01-06","cited_by_count":11},{"id":"2402.10744","title":"GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models","created":"2024-02-16","cited_by_count":7},{"id":"2007.04247","title":"Neural relation extraction: a survey","created":"2020-06-23","cited_by_count":4},{"id":"2212.08216","title":"Azimuth: Systematic Error Analysis for Text Classification","created":"2022-12-16","cited_by_count":4},{"id":"2405.20787","title":"PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction","created":"2024-05-30","cited_by_count":3}],"next_cursor":null}