{"list":"other","total":51,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"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":"1606.05250","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","created":"2016-06-16","cited_by_count":4148},{"id":"2004.10964","title":"Don't Stop Pretraining: Adapt Language Models to Domains and Tasks","created":"2020-04-23","cited_by_count":1565},{"id":"1909.06146","title":"PubMedQA: A Dataset for Biomedical Research Question Answering","created":"2019-09-13","cited_by_count":1288},{"id":"1911.00172","title":"Generalization through Memorization: Nearest Neighbor Language Models","created":"2019-11-01","cited_by_count":800},{"id":"2010.00710","title":"Nearest Neighbor Machine Translation","created":"2020-10-01","cited_by_count":209},{"id":"2105.06762","title":"DialogSum: A Real-Life Scenario Dialogue Summarization Dataset","created":"2021-05-14","cited_by_count":183},{"id":"2106.01760","title":"Template-Based Named Entity Recognition Using BART","created":"2021-06-03","cited_by_count":153},{"id":"2007.08426","title":"Investigating Pretrained Language Models for Graph-to-Text Generation","created":"2020-07-16","cited_by_count":117},{"id":"1911.07470","title":"Graph Transformer for Graph-to-Sequence Learning","created":"2019-11-18","cited_by_count":82},{"id":"2109.13532","title":"Template-free Prompt Tuning for Few-shot NER","created":"2021-09-28","cited_by_count":73},{"id":"1704.08381","title":"Neural AMR: Sequence-to-Sequence Models for Parsing and Generation","created":"2017-04-26","cited_by_count":56},{"id":"1805.02473","title":"A Graph-to-Sequence Model for AMR-to-Text Generation","created":"2018-05-07","cited_by_count":56},{"id":"2203.07836","title":"Graph Pre-training for AMR Parsing and Generation","created":"2022-03-15","cited_by_count":55},{"id":"1905.08704","title":"AMR Parsing as Sequence-to-Graph Transduction","created":"2019-05-21","cited_by_count":50},{"id":"2004.05572","title":"AMR Parsing via Graph-Sequence Iterative Inference","created":"2020-04-12","cited_by_count":47},{"id":"1805.10399","title":"Toward Abstractive Summarization Using Semantic Representations","created":"2018-05-25","cited_by_count":43},{"id":"2010.10669","title":"Transition-based Parsing with Stack-Transformers","created":"2020-10-20","cited_by_count":38},{"id":"1902.07282","title":"Semantic Neural Machine Translation using AMR","created":"2019-02-19","cited_by_count":37},{"id":"1805.05286","title":"AMR Parsing as Graph Prediction with Latent Alignment","created":"2018-05-14","cited_by_count":36},{"id":"1806.05655","title":"Abstract Meaning Representation for Multi-Document Summarization","created":"2018-06-14","cited_by_count":35},{"id":"1909.00136","title":"Modeling Graph Structure in Transformer for Better AMR-to-Text Generation","created":"2019-08-31","cited_by_count":35},{"id":"1608.06111","title":"An Incremental Parser for Abstract Meaning Representation","created":"2016-08-22","cited_by_count":34},{"id":"2202.00217","title":"WebFormer: The Web-page Transformer for Structure Information Extraction","created":"2022-02-01","cited_by_count":34},{"id":"1909.02607","title":"Broad-Coverage Semantic Parsing as Transduction","created":"2019-09-05","cited_by_count":32},{"id":"1703.06345","title":"Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks","created":"2017-03-18","cited_by_count":30},{"id":"2202.04824","title":"AdaPrompt: Adaptive Model Training for Prompt-based NLP","created":"2022-02-10","cited_by_count":30},{"id":"2112.07790","title":"Maximum Bayes Smatch Ensemble Distillation for AMR Parsing","created":"2021-12-14","cited_by_count":29},{"id":"2208.09770","title":"Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization","created":"2022-08-21","cited_by_count":29},{"id":"1906.04225","title":"Label-Agnostic Sequence Labeling by Copying Nearest Neighbors","created":"2019-06-10","cited_by_count":27},{"id":"1905.13370","title":"Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning","created":"2019-05-31","cited_by_count":25},{"id":"2104.14674","title":"AMR Parsing with Action-Pointer Transformer","created":"2021-04-29","cited_by_count":25},{"id":"2110.15534","title":"Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing","created":"2021-10-29","cited_by_count":25},{"id":"1909.04303","title":"Core Semantic First: A Top-down Approach for AMR Parsing","created":"2019-09-10","cited_by_count":24},{"id":"2105.10188","title":"Semantic Representation for Dialogue Modeling","created":"2021-05-21","cited_by_count":24},{"id":"1705.09980","title":"Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations","created":"2017-05-28","cited_by_count":18},{"id":"2010.10673","title":"Pushing the Limits of AMR Parsing with Self-Learning","created":"2020-10-20","cited_by_count":16},{"id":"1707.07755","title":"AMR Parsing using Stack-LSTMs","created":"2017-07-24","cited_by_count":15},{"id":"1704.00552","title":"A Transition-Based Directed Acyclic Graph Parser for UCCA","created":"2017-04-03","cited_by_count":13},{"id":"1810.03541","title":"An AMR Aligner Tuned by Transition-based Parser","created":"2018-10-08","cited_by_count":12},{"id":"1604.01278","title":"RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy","created":"2016-04-05","cited_by_count":11},{"id":"1702.05053","title":"Addressing the Data Sparsity Issue in Neural AMR Parsing","created":"2017-02-16","cited_by_count":11},{"id":"1805.00287","title":"Multitask Parsing Across Semantic Representations","created":"2018-05-01","cited_by_count":10},{"id":"2010.04520","title":"Online Back-Parsing for AMR-to-Text Generation","created":"2020-10-09","cited_by_count":9},{"id":"2204.06893","title":"Challenges for Open-domain Targeted Sentiment Analysis","created":"2022-04-14","cited_by_count":9},{"id":"2205.00379","title":"The Cross-lingual Conversation Summarization Challenge","created":"2022-05-01","cited_by_count":8},{"id":"2110.06920","title":"Semantics-aware Attention Improves Neural Machine Translation","created":"2021-10-13","cited_by_count":5},{"id":"1805.09007","title":"A Transition-based Algorithm for Unrestricted AMR Parsing","created":"2018-05-23","cited_by_count":4},{"id":"2109.12814","title":"Investigating Non-local Features for Neural Constituency Parsing","created":"2021-09-27","cited_by_count":4},{"id":"2209.09146","title":"Semantic-based Pre-training for Dialogue Understanding","created":"2022-09-19","cited_by_count":3}],"next_cursor":null}