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Dietrich Klakow | Jie Zhou | Xiaoyu Shen | Ernie Chang | Hui Su | Jie Zhou | Hui Su | D. Klakow | Xiaoyu Shen | Ernie Chang
[1] Dietrich Klakow,et al. Estimation of Gap Between Current Language Models and Human Performance , 2017, INTERSPEECH.
[2] Hermann Ney,et al. Neural Hidden Markov Model for Machine Translation , 2018, ACL.
[3] Dietrich Klakow,et al. Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator , 2019, EMNLP.
[4] Emiel Krahmer,et al. Neural data-to-text generation: A comparison between pipeline and end-to-end architectures , 2019, EMNLP.
[5] Kentaro Inui,et al. Select and Attend: Towards Controllable Content Selection in Text Generation , 2019, EMNLP.
[6] Jason Eisner,et al. Inside-Outside and Forward-Backward Algorithms Are Just Backprop (tutorial paper) , 2016, SPNLP@EMNLP.
[7] Michael Backes,et al. Simulating the Large-Scale Erosion of Genomic Privacy Over Time , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[8] David Grangier,et al. Neural Text Generation from Structured Data with Application to the Biography Domain , 2016, EMNLP.
[9] Dan Klein,et al. Learning Semantic Correspondences with Less Supervision , 2009, ACL.
[10] Alexander M. Rush,et al. Learning Neural Templates for Text Generation , 2018, EMNLP.
[11] Xiaoyu Shen,et al. Unsupervised Pidgin Text Generation By Pivoting English Data and Self-Training , 2020, ArXiv.
[12] Blake Howald,et al. A Statistical NLG Framework for Aggregated Planning and Realization , 2013, ACL.
[13] Salim Roukos,et al. Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.
[14] Karen Kukich,et al. Design of a Knowledge-Based Report Generator , 1983, ACL.
[15] Ben Taskar,et al. Posterior Regularization for Structured Latent Variable Models , 2010, J. Mach. Learn. Res..
[16] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[17] Sunita Sarawagi,et al. Posterior Attention Models for Sequence to Sequence Learning , 2019, ICLR.
[18] Alexander M. Rush,et al. Structured Attention Networks , 2017, ICLR.
[19] Kartikeya Upasani,et al. Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue , 2019, ACL.
[20] Zhoujun Li,et al. Low-Resource Response Generation with Template Prior , 2019, EMNLP/IJCNLP.
[21] Chin-Yew Lin,et al. ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.
[22] Alexander M. Rush,et al. Coarse-to-Fine Attention Models for Document Summarization , 2017, NFiS@EMNLP.
[23] Iryna Gurevych,et al. E2E NLG Challenge: Neural Models vs. Templates , 2018, INLG.
[24] Dan Klein,et al. A Simple Domain-Independent Probabilistic Approach to Generation , 2010, EMNLP.
[25] Hermann Ney,et al. Improved Alignment Models for Statistical Machine Translation , 1999, EMNLP.
[26] Mirella Lapata,et al. Data-to-Text Generation with Content Selection and Planning , 2018, AAAI.
[27] Alexander M. Rush,et al. Latent Alignment and Variational Attention , 2018, NeurIPS.
[28] Ryan Cotterell,et al. Hard Non-Monotonic Attention for Character-Level Transduction , 2018, EMNLP.
[29] Robert L. Mercer,et al. The Mathematics of Statistical Machine Translation: Parameter Estimation , 1993, CL.
[30] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[31] Daniel Marcu,et al. Induction of Word and Phrase Alignments for Automatic Document Summarization , 2005, CL.
[32] Colin Raffel,et al. Monotonic Chunkwise Attention , 2017, ICLR.
[33] C. Lawrence Zitnick,et al. CIDEr: Consensus-based image description evaluation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Lei Yu,et al. Online Segment to Segment Neural Transduction , 2016, EMNLP.
[35] Anja Belz,et al. Comparing Automatic and Human Evaluation of NLG Systems , 2006, EACL.
[36] Chong Wang,et al. Sequence Modeling via Segmentations , 2017, ICML.
[37] Verena Rieser,et al. Why We Need New Evaluation Metrics for NLG , 2017, EMNLP.
[38] Alexander M. Rush,et al. Image-to-Markup Generation with Coarse-to-Fine Attention , 2016, ICML.
[39] Dietrich Klakow,et al. NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation , 2018, EMNLP.
[40] Raymond J. Mooney,et al. Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision , 2010, COLING.
[41] Robert Dale,et al. Building applied natural language generation systems , 1997, Natural Language Engineering.
[42] Verena Rieser,et al. The E2E Dataset: New Challenges For End-to-End Generation , 2017, SIGDIAL Conference.
[43] Verena Rieser,et al. Findings of the E2E NLG Challenge , 2018, INLG.
[44] Cheng Niu,et al. Improving Multi-turn Dialogue Modelling with Utterance ReWriter , 2019, ACL.
[45] Mirella Lapata,et al. A Global Model for Concept-to-Text Generation , 2013, J. Artif. Intell. Res..
[46] Anja Belz,et al. Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models , 2008, Natural Language Engineering.
[47] Mirella Lapata,et al. Collective Content Selection for Concept-to-Text Generation , 2005, HLT.
[48] Alexander M. Rush,et al. A Tutorial on Deep Latent Variable Models of Natural Language , 2018, ArXiv.
[49] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[50] Chris Dyer,et al. Unsupervised Word Discovery with Segmental Neural Language Models , 2018, ArXiv.
[51] Christopher D. Manning,et al. Get To The Point: Summarization with Pointer-Generator Networks , 2017, ACL.
[52] Jason Eisner,et al. Parameter Estimation for Probabilistic Finite-State Transducers , 2002, ACL.
[53] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[54] Alon Lavie,et al. METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments , 2005, IEEvaluation@ACL.
[55] Anja Belz,et al. An Investigation into the Validity of Some Metrics for Automatically Evaluating Natural Language Generation Systems , 2009, CL.
[56] Claire Gardent,et al. The WebNLG Challenge: Generating Text from DBPedia Data , 2016, INLG.
[57] Giuseppe Carenini,et al. A Template-based Abstractive Meeting Summarization: Leveraging Summary and Source Text Relationships , 2014, INLG.
[58] Andreas Vlachos,et al. Sheffield at E2E: structured prediction approaches to end-to-end language generation. , 2018 .
[59] Christopher D. Manning,et al. Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.
[60] Guy Lapalme,et al. Text generation , 1990 .
[61] Chin-Yew Lin,et al. Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data , 2018, EMNLP.
[62] Ido Dagan,et al. Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation , 2019, NAACL.
[63] Matthew R. Walter,et al. What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment , 2015, NAACL.
[64] Marilyn A. Walker,et al. Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring? , 2018, INLG.
[65] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[66] Alexander M. Rush,et al. Challenges in Data-to-Document Generation , 2017, EMNLP.
[67] Marilyn A. Walker,et al. A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation , 2018, NAACL.
[68] Verena Rieser,et al. Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge , 2019, Comput. Speech Lang..
[69] Ali Farhadi,et al. Multi-Resolution Language Grounding with Weak Supervision , 2014, EMNLP.
[70] Yang Zhao,et al. A comprehensive study: Sentence compression with linguistic knowledge-enhanced gated neural network , 2018, Data Knowl. Eng..