暂无分享,去创建一个
Tao Yu | Rui Zhang | Dragomir R. Radev | Xiangru Tang | Richard Socher | Caiming Xiong | Nazneen Fatema Rajani | Ankit Gupta | Dragomir Radev | Yi Chern Tan | Xi Victoria Lin | Jessica Pan | Neha Verma | Amrit Rau | Abhinand Sivaprasad | Chiachun Hsieh | Aadit Vyas | Pranav Krishna | Yangxiaokang Liu | Nadia Irwanto | Faiaz Rahman | Ahmad Zaidi | Murori Mutuma | Yasin Tarabar | R. Socher | Caiming Xiong | Y. Tan | Tao Yu | Rui Zhang | Nazneen Rajani | P. Krishna | Xiangru Tang | Faiaz Rahman | Ankit Gupta | Nadia Irwanto | Amrit Rau | Neha Verma | Abhinand Sivaprasad | Chia-Hsuan Hsieh | Aadit Vyas | Yangxiaokang Liu | Jessica Pan | Murori Mutuma | Yasin Tarabar | A. Zaidi
[1] Mirella Lapata,et al. Text Generation from Knowledge Graphs with Graph Transformers , 2019, NAACL.
[2] Ido Dagan,et al. Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation , 2019, NAACL.
[3] Jim Hunter,et al. Choosing words in computer-generated weather forecasts , 2005, Artif. Intell..
[4] Zhifang Sui,et al. Table-to-text Generation by Structure-aware Seq2seq Learning , 2017, AAAI.
[5] Lav R. Varshney,et al. CTRL: A Conditional Transformer Language Model for Controllable Generation , 2019, ArXiv.
[6] Mirella Lapata,et al. Data-to-Text Generation with Content Selection and Planning , 2018, AAAI.
[7] Lu Chen,et al. Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks , 2020, ACL.
[8] Dong Yu,et al. Structural Information Preserving for Graph-to-Text Generation , 2020, ACL.
[9] Jimmy Lin,et al. Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data , 2020, ACL.
[10] Richard Socher,et al. Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning , 2018, ArXiv.
[11] Matt J. Kusner,et al. From Word Embeddings To Document Distances , 2015, ICML.
[12] Xiaodong Liu,et al. Unified Language Model Pre-training for Natural Language Understanding and Generation , 2019, NeurIPS.
[13] Heng Ji,et al. Describing a Knowledge Base , 2018, INLG.
[14] Claire Gardent,et al. The WebNLG Challenge: Generating Text from RDF Data , 2017, INLG.
[15] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[16] Kilian Q. Weinberger,et al. BERTScore: Evaluating Text Generation with BERT , 2019, ICLR.
[17] Claire Gardent,et al. Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs , 2019, EMNLP.
[18] Emiel Krahmer,et al. Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation , 2017, INLG.
[19] Alon Lavie,et al. Meteor Universal: Language Specific Translation Evaluation for Any Target Language , 2014, WMT@ACL.
[20] Salim Roukos,et al. Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.
[21] Wenhu Chen,et al. Logical Natural Language Generation from Open-Domain Tables , 2020, ACL.
[22] Matthew R. Walter,et al. What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment , 2015, NAACL.
[23] Verena Rieser,et al. The E2E Dataset: New Challenges For End-to-End Generation , 2017, SIGDIAL Conference.
[24] Verena Rieser,et al. Findings of the E2E NLG Challenge , 2018, INLG.
[25] Iryna Gurevych,et al. Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs , 2020, Transactions of the Association for Computational Linguistics.
[26] David Vandyke,et al. Conditional Generation and Snapshot Learning in Neural Dialogue Systems , 2016, EMNLP.
[27] Tiejun Zhao,et al. Table-to-Text: Describing Table Region With Natural Language , 2018, AAAI.
[28] Verena Rieser,et al. Why We Need New Evaluation Metrics for NLG , 2017, EMNLP.
[29] Yue Zhang,et al. AMR-to-text Generation with Synchronous Node Replacement Grammar , 2017, ACL.
[30] Claire Gardent,et al. The KBGen Challenge , 2013, ENLG.
[31] Christophe Gravier,et al. T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples , 2018, LREC.
[32] Mirella Lapata,et al. Unsupervised Concept-to-text Generation with Hypergraphs , 2012, NAACL.
[33] Mirella Lapata,et al. Data-to-text Generation with Entity Modeling , 2019, ACL.
[34] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[35] Lucia Specia,et al. WMDO: Fluency-based Word Mover’s Distance for Machine Translation Evaluation , 2019, WMT.
[36] Alexander M. Rush,et al. Challenges in Data-to-Document Generation , 2017, EMNLP.
[37] Ido Dagan,et al. Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation , 2019, INLG.
[38] Dragomir R. Radev,et al. ESPRIT: Explaining Solutions to Physical Reasoning Tasks , 2020, ACL.
[39] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[40] Zhiyu Chen,et al. Few-shot NLG with Pre-trained Language Model , 2020, ACL.
[41] Jianfeng Gao,et al. DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation , 2020, ACL.
[42] Percy Liang,et al. Transforming Question Answering Datasets Into Natural Language Inference Datasets , 2018, ArXiv.
[43] R. B. Jones,et al. Natural language generation in health care. , 1997, Journal of the American Medical Informatics Association : JAMIA.
[44] Alexander M. Rush,et al. End-to-End Content and Plan Selection for Data-to-Text Generation , 2018, INLG.
[45] Ralph Weischedel,et al. A STUDY OF TRANSLATION ERROR RATE WITH TARGETED HUMAN ANNOTATION , 2005 .
[46] Yejin Choi,et al. CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning , 2020, EMNLP.
[47] Ehud Reiter,et al. Book Reviews: Building Natural Language Generation Systems , 2000, CL.
[48] Claire Gardent,et al. Handling Rare Items in Data-to-Text Generation , 2018, INLG.
[49] Chris Mellish,et al. Optimising text quality in generation from relational databases , 2000, INLG.
[50] Iryna Gurevych,et al. Enhancing AMR-to-Text Generation with Dual Graph Representations , 2019, EMNLP.
[51] Diyi Yang,et al. ToTTo: A Controlled Table-To-Text Generation Dataset , 2020, EMNLP.
[52] Hiroya Takamura,et al. Generating Live Soccer-Match Commentary from Play Data , 2019, AAAI.
[53] Verena Rieser,et al. Semantic Noise Matters for Neural Natural Language Generation , 2019, INLG.
[54] Chi-kiu Lo,et al. YiSi - a Unified Semantic MT Quality Evaluation and Estimation Metric for Languages with Different Levels of Available Resources , 2019, WMT.
[55] Xiaojun Wan,et al. AMR-To-Text Generation with Graph Transformer , 2020, TACL.
[56] Hongmin Wang,et al. Revisiting Challenges in Data-to-Text Generation with Fact Grounding , 2020, INLG.
[57] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[58] David Vandyke,et al. Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems , 2015, EMNLP.
[59] Kôiti Hasida,et al. Reactive Content Selection in the Generation of Real-time Soccer Commentary , 1998, COLING-ACL.
[60] Alec Radford,et al. Improving Language Understanding by Generative Pre-Training , 2018 .
[61] Patrick Gallinari,et al. Copy mechanism and tailored training for character-based data-to-text generation , 2019, ECML/PKDD.
[62] Christophe Gravier,et al. Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples , 2017, J. Web Semant..
[63] Dan Klein,et al. Pragmatically Informative Text Generation , 2019, NAACL.
[64] Ankur Parikh,et al. Handling Divergent Reference Texts when Evaluating Table-to-Text Generation , 2019, ACL.
[65] Guodong Zhou,et al. Modeling Graph Structure in Transformer for Better AMR-to-Text Generation , 2019, EMNLP.
[66] Jianfeng Gao,et al. Few-shot Natural Language Generation for Task-Oriented Dialog , 2020, FINDINGS.
[67] Percy Liang,et al. Compositional Semantic Parsing on Semi-Structured Tables , 2015, ACL.
[68] Raymond J. Mooney,et al. Learning to sportscast: a test of grounded language acquisition , 2008, ICML '08.
[69] Yusuke Miyao,et al. Learning to Select, Track, and Generate for Data-to-Text , 2019, ACL.
[70] Chin-Yew Lin,et al. ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.
[71] Amir Saffari,et al. Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity , 2020, COLING.
[72] Diego Marcheggiani,et al. Deep Graph Convolutional Encoders for Structured Data to Text Generation , 2018, INLG.
[73] Shashi Narayan,et al. Leveraging Pre-trained Checkpoints for Sequence Generation Tasks , 2019, Transactions of the Association for Computational Linguistics.
[74] Pascal Poupart,et al. Order-Planning Neural Text Generation From Structured Data , 2017, AAAI.
[75] Alvin Cheung,et al. Summarizing Source Code using a Neural Attention Model , 2016, ACL.
[76] Wenhu Chen,et al. Logic2Text: High-Fidelity Natural Language Generation from Logical Forms , 2020, EMNLP.
[77] Liheng Chen,et al. Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence , 2019, SIGIR.
[78] Mihir Kale. Text-to-Text Pre-Training for Data-to-Text Tasks , 2020, INLG.
[79] Shay B. Cohen,et al. Structural Neural Encoders for AMR-to-text Generation , 2019, NAACL.
[80] Alon Lavie,et al. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments , 2005, IEEvaluation@ACL.
[81] Omer Levy,et al. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension , 2019, ACL.
[82] Mirella Lapata,et al. Bootstrapping Generators from Noisy Data , 2018, NAACL.
[83] Shashi Narayan,et al. Creating Training Corpora for NLG Micro-Planners , 2017, ACL.
[84] Hao Zhou,et al. Variational Template Machine for Data-to-Text Generation , 2020, ICLR.
[85] Emiel Krahmer,et al. Neural data-to-text generation: A comparison between pipeline and end-to-end architectures , 2019, EMNLP.
[86] Emiel Krahmer,et al. Enriching the WebNLG corpus , 2018, INLG.
[87] Wei Wang,et al. GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data , 2018, ACL.
[88] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[89] Snigdha Chaturvedi,et al. Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation , 2020, ACL.
[90] Dietrich Klakow,et al. Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence , 2020, ACL.
[91] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[92] Richard I. Kittredge,et al. Using natural-language processing to produce weather forecasts , 1994, IEEE Expert.
[93] Jaime G. Carbonell,et al. Generation from Abstract Meaning Representation using Tree Transducers , 2016, NAACL.
[94] Fei Liu,et al. MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance , 2019, EMNLP.
[95] Thibault Sellam,et al. BLEURT: Learning Robust Metrics for Text Generation , 2020, ACL.
[96] David Grangier,et al. Neural Text Generation from Structured Data with Application to the Biography Domain , 2016, EMNLP.
[97] Dong Yu,et al. Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints , 2020, ACL.
[98] Dan Klein,et al. Learning Semantic Correspondences with Less Supervision , 2009, ACL.
[99] Luyao Chen,et al. CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases , 2019, EMNLP.