{"list":"other","total":76,"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":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"2005.11401","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","created":"2020-05-22","cited_by_count":9297},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1301.3781","title":"Efficient Estimation of Word Representations in Vector Space","created":"2013-01-16","cited_by_count":4375},{"id":"1310.4546","title":"Distributed Representations of Words and Phrases and their Compositionality","created":"2013-10-16","cited_by_count":3474},{"id":"1906.08237","title":"XLNet: Generalized Autoregressive Pretraining for Language Understanding","created":"2019-06-19","cited_by_count":2811},{"id":"1802.05365","title":"Deep contextualized word representations","created":"2018-02-15","cited_by_count":2290},{"id":"2107.13586","title":"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","created":"2021-07-28","cited_by_count":2234},{"id":"2002.08909","title":"REALM: Retrieval-Augmented Language Model Pre-Training","created":"2020-02-10","cited_by_count":2141},{"id":"1909.01066","title":"Language Models as Knowledge Bases?","created":"2019-09-03","cited_by_count":1936},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"1607.04606","title":"Enriching Word Vectors with Subword Information","created":"2016-07-15","cited_by_count":1583},{"id":"1611.07308","title":"Variational Graph Auto-Encoders","created":"2016-11-21","cited_by_count":1382},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1906.04341","title":"What Does BERT Look At? An Analysis of BERT's Attention","created":"2019-06-11","cited_by_count":1003},{"id":"1412.6575","title":"Embedding Entities and Relations for Learning and Inference in Knowledge Bases","created":"2014-12-20","cited_by_count":932},{"id":"2002.12327","title":"A Primer in BERTology: What we know about how BERT works","created":"2020-02-27","cited_by_count":815},{"id":"1707.01476","title":"Convolutional 2D Knowledge Graph Embeddings","created":"2017-07-05","cited_by_count":730},{"id":"2002.08910","title":"How Much Knowledge Can You Pack Into the Parameters of a Language Model?","created":"2020-02-10","cited_by_count":719},{"id":"2002.00388","title":"A Survey on Knowledge Graphs: Representation, Acquisition and Applications","created":"2020-02-02","cited_by_count":643},{"id":"1905.07129","title":"ERNIE: Enhanced Language Representation with Informative Entities","created":"2019-05-17","cited_by_count":528},{"id":"2003.08271","title":"Pre-trained Models for Natural Language Processing: A Survey","created":"2020-03-18","cited_by_count":505},{"id":"1906.05317","title":"COMET: Commonsense Transformers for Automatic Knowledge Graph Construction","created":"2019-06-12","cited_by_count":450},{"id":"1706.04115","title":"Zero-Shot Relation Extraction via Reading Comprehension","created":"2017-06-13","cited_by_count":426},{"id":"1911.03082","title":"Composition-based Multi-Relational Graph Convolutional Networks","created":"2019-11-08","cited_by_count":396},{"id":"1808.09121","title":"WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations","created":"2018-08-28","cited_by_count":372},{"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":"1506.06726","title":"Skip-Thought Vectors","created":"2015-06-22","cited_by_count":322},{"id":"2002.01808","title":"K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters","created":"2020-02-05","cited_by_count":319},{"id":"1909.07606","title":"K-BERT: Enabling Language Representation with Knowledge Graph","created":"2019-09-17","cited_by_count":304},{"id":"1904.09223","title":"ERNIE: Enhanced Representation through Knowledge Integration","created":"2019-04-19","cited_by_count":302},{"id":"1908.05739","title":"Abductive Commonsense Reasoning","created":"2019-08-15","cited_by_count":301},{"id":"1909.04164","title":"Knowledge Enhanced Contextual Word Representations","created":"2019-09-09","cited_by_count":296},{"id":"2105.11259","title":"PTR: Prompt Tuning with Rules for Text Classification","created":"2021-05-24","cited_by_count":238},{"id":"1907.12412","title":"ERNIE 2.0: A Continual Pre-training Framework for Language Understanding","created":"2019-07-29","cited_by_count":231},{"id":"2002.06177","title":"The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence","created":"2020-02-14","cited_by_count":195},{"id":"1510.04935","title":"Holographic Embeddings of Knowledge Graphs","created":"2015-10-16","cited_by_count":181},{"id":"1606.05433","title":"FVQA: Fact-based Visual Question Answering","created":"2016-06-17","cited_by_count":181},{"id":"1912.13283","title":"oLMpics -- On what Language Model Pre-training Captures","created":"2019-12-31","cited_by_count":161},{"id":"1811.04441","title":"End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion","created":"2018-11-11","cited_by_count":136},{"id":"1906.01195","title":"Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs","created":"2019-06-04","cited_by_count":125},{"id":"1903.09588","title":"Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence","created":"2019-03-22","cited_by_count":122},{"id":"2001.05139","title":"A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation","created":"2020-01-15","cited_by_count":116},{"id":"2106.09231","title":"Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases","created":"2021-06-17","cited_by_count":107},{"id":"2009.12677","title":"KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning","created":"2020-09-26","cited_by_count":106},{"id":"1912.09637","title":"Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model","created":"2019-12-20","cited_by_count":102},{"id":"2010.00309","title":"CoLAKE: Contextualized Language and Knowledge Embedding","created":"2020-10-01","cited_by_count":86},{"id":"1511.02570","title":"Explicit Knowledge-based Reasoning for Visual Question Answering","created":"2015-11-09","cited_by_count":85},{"id":"1908.05646","title":"SenseBERT: Driving Some Sense into BERT","created":"2019-08-15","cited_by_count":73},{"id":"2012.15022","title":"ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning","created":"2020-12-30","cited_by_count":72},{"id":"1906.07241","title":"Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling","created":"2019-06-17","cited_by_count":71},{"id":"1909.05311","title":"Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering","created":"2019-09-09","cited_by_count":68},{"id":"1612.04844","title":"The More You Know: Using Knowledge Graphs for Image Classification","created":"2016-12-14","cited_by_count":63},{"id":"2010.03496","title":"Inductive Entity Representations from Text via Link Prediction","created":"2020-10-07","cited_by_count":62},{"id":"1301.3485","title":"A Semantic Matching Energy Function for Learning with Multi-relational Data","created":"2013-01-15","cited_by_count":61},{"id":"2009.11692","title":"Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph","created":"2020-09-24","cited_by_count":57},{"id":"2005.05635","title":"SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis","created":"2020-05-12","cited_by_count":54},{"id":"2103.04350","title":"Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees","created":"2021-03-07","cited_by_count":51},{"id":"2003.07278","title":"A Survey on Contextual Embeddings","created":"2020-03-16","cited_by_count":48},{"id":"2008.09084","title":"Do Syntax Trees Help Pre-trained Transformers Extract Information?","created":"2020-08-20","cited_by_count":48},{"id":"1911.00720","title":"ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations","created":"2019-11-02","cited_by_count":35},{"id":"1912.00147","title":"Integrating Graph Contextualized Knowledge into Pre-trained Language Models","created":"2019-11-30","cited_by_count":26},{"id":"2004.14224","title":"Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning","created":"2020-04-29","cited_by_count":26},{"id":"2109.13006","title":"RuleBert: Teaching Soft Rules to Pre-trained Language Models","created":"2021-09-24","cited_by_count":25},{"id":"1909.02339","title":"Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity","created":"2019-09-05","cited_by_count":20},{"id":"2004.12006","title":"Contextualized Representations Using Textual Encyclopedic Knowledge","created":"2020-04-24","cited_by_count":17},{"id":"2008.03945","title":"On Commonsense Cues in BERT for Solving Commonsense Tasks","created":"2020-08-10","cited_by_count":13},{"id":"2009.13964","title":"CokeBERT: Contextual Knowledge Selection and Embedding towards Enhanced Pre-Trained Language Models","created":"2020-09-29","cited_by_count":11},{"id":"1911.02821","title":"Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention","created":"2019-11-07","cited_by_count":7}],"next_cursor":null}