{"list":"other","total":54,"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":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"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":"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":"1904.09751","title":"The Curious Case of Neural Text Degeneration","created":"2019-04-22","cited_by_count":2916},{"id":"1910.03771","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","created":"2019-10-09","cited_by_count":2218},{"id":"2202.03629","title":"Survey of Hallucination in Natural Language Generation","created":"2022-02-08","cited_by_count":2075},{"id":"1904.01038","title":"fairseq: A Fast, Extensible Toolkit for Sequence Modeling","created":"2019-04-01","cited_by_count":1488},{"id":"1808.08745","title":"Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization","created":"2018-08-27","cited_by_count":1317},{"id":"1506.06724","title":"Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books","created":"2015-06-22","cited_by_count":1043},{"id":"2005.00661","title":"On Faithfulness and Factuality in Abstractive Summarization","created":"2020-05-02","cited_by_count":900},{"id":"1801.07243","title":"Personalizing Dialogue Agents: I have a dog, do you have pets too?","created":"2018-01-22","cited_by_count":828},{"id":"1506.03099","title":"Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks","created":"2015-06-09","cited_by_count":803},{"id":"1606.07947","title":"Sequence-Level Knowledge Distillation","created":"2016-06-25","cited_by_count":801},{"id":"2104.07567","title":"Retrieval Augmentation Reduces Hallucination in Conversation","created":"2021-04-15","cited_by_count":692},{"id":"1511.06732","title":"Sequence Level Training with Recurrent Neural Networks","created":"2015-11-20","cited_by_count":576},{"id":"1711.02281","title":"Non-Autoregressive Neural Machine Translation","created":"2017-11-07","cited_by_count":463},{"id":"1604.01696","title":"A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories","created":"2016-04-06","cited_by_count":417},{"id":"1705.04304","title":"A Deep Reinforced Model for Abstractive Summarization","created":"2017-05-11","cited_by_count":365},{"id":"1905.02450","title":"MASS: Masked Sequence to Sequence Pre-training for Language Generation","created":"2019-05-07","cited_by_count":347},{"id":"2001.04063","title":"ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training","created":"2020-01-13","cited_by_count":222},{"id":"1905.11006","title":"Levenshtein Transformer","created":"2019-05-27","cited_by_count":199},{"id":"1802.06901","title":"Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement","created":"2018-02-19","cited_by_count":195},{"id":"1906.01081","title":"Handling Divergent Reference Texts when Evaluating Table-to-Text Generation","created":"2019-06-03","cited_by_count":119},{"id":"2001.05139","title":"A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation","created":"2020-01-15","cited_by_count":116},{"id":"2204.01171","title":"Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation","created":"2022-04-03","cited_by_count":108},{"id":"2008.07905","title":"Glancing Transformer for Non-Autoregressive Neural Machine Translation","created":"2020-08-18","cited_by_count":103},{"id":"2012.15833","title":"Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade","created":"2020-12-31","cited_by_count":99},{"id":"2107.01294","title":"Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text","created":"2021-07-02","cited_by_count":89},{"id":"1811.04719","title":"End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification","created":"2018-11-12","cited_by_count":72},{"id":"2004.01655","title":"Aligned Cross Entropy for Non-Autoregressive Machine Translation","created":"2020-04-03","cited_by_count":65},{"id":"1910.11555","title":"Fast Structured Decoding for Sequence Models","created":"2019-10-25","cited_by_count":59},{"id":"2106.05093","title":"Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation","created":"2021-06-09","cited_by_count":57},{"id":"2012.14583","title":"Understanding and Improving Lexical Choice in Non-Autoregressive Translation","created":"2020-12-29","cited_by_count":56},{"id":"2005.00969","title":"Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints","created":"2020-05-03","cited_by_count":48},{"id":"2011.11928","title":"GLGE: A New General Language Generation Evaluation Benchmark","created":"2020-11-24","cited_by_count":47},{"id":"2205.07459","title":"Directed Acyclic Transformer for Non-Autoregressive Machine Translation","created":"2022-05-16","cited_by_count":44},{"id":"2110.07515","title":"Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision","created":"2021-10-14","cited_by_count":40},{"id":"2010.06138","title":"Incorporating BERT into Parallel Sequence Decoding with Adapters","created":"2020-10-13","cited_by_count":39},{"id":"2102.08220","title":"Non-Autoregressive Text Generation with Pre-trained Language Models","created":"2021-02-16","cited_by_count":36},{"id":"2106.00903","title":"Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation","created":"2021-06-02","cited_by_count":34},{"id":"2012.15525","title":"BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining","created":"2020-12-31","cited_by_count":33},{"id":"1909.06708","title":"Hint-Based Training for Non-Autoregressive Machine Translation","created":"2019-09-15","cited_by_count":28},{"id":"2206.05975","title":"On the Learning of Non-Autoregressive Transformers","created":"2022-06-13","cited_by_count":27},{"id":"2204.02030","title":"$\\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation","created":"2022-04-05","cited_by_count":20},{"id":"2205.14333","title":"One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation","created":"2022-05-28","cited_by_count":20},{"id":"2110.05722","title":"LightSeq2: Accelerated Training for Transformer-based Models on GPUs","created":"2021-10-12","cited_by_count":19},{"id":"2210.13304","title":"ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation","created":"2022-10-24","cited_by_count":10},{"id":"2210.03999","title":"ngram-OAXE: Phrase-Based Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation","created":"2022-10-08","cited_by_count":8},{"id":"2106.02210","title":"NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer","created":"2021-06-04","cited_by_count":7},{"id":"2110.11115","title":"Improving Non-autoregressive Generation with Mixup Training","created":"2021-10-21","cited_by_count":7}],"next_cursor":null}