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Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand natural language.
Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, William W Cohen, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov. 2019 · 1901
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An adversarial approach to high-quality, sentiment-controlled neural dialogue generation
Xiang Kong, Bohan Li, Graham Neubig, Eduard Hovy, and Yiming Yang. 2019a · 1901
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Reference-less quality estimation of text simplification systems
Louis Martin, Samuel Humeau, Pierre-Emmanuel Mazaré, Antoine Bordes, Éric Villemonte de La Clergerie, and Benoît Sagot. 2019a · 1901
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Transfertransfo: A transfer learning approach for neural network based conversational agents
Thomas Wolf, Victor Sanh, Julien Chaumond, and Clement Delangue. 2019 · 1901
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Learning and evaluating general linguistic intelligence
Dani Yogatama, Cyprien de Masson d’Autume, Jerome Connor, Tomas Kocisky, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, et al. 2019 · 1901
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Personalized dialogue generation with diversified traits
Yinhe Zheng, Guanyi Chen, Minlie Huang, Song Liu, and Xuan Zhu. 2019 · 1901
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Attention, please! a critical review of neural attention models in natural language processing
Andrea Galassi, Marco Lippi, and Paolo Torroni. 2019 · 1902
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Insertion-based decoding with automatically inferred generation order
Jiatao Gu, Qi Liu, and Kyunghyun Cho. 2019a · 1902
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
R Thomas McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 1902
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Non-monotonic sequential text generation
Sean Welleck, Kianté Brantley, Hal Daumé III, and Kyunghyun Cho. 2019a · 1902
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Adversarial generation of handwritten text images conditioned on sequences
Eloi Alonso, Bastien Moysset, and Ronaldo Messina. 2019 · 1903
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019 · 1904
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Unifying human and statistical evaluation for natural language generation
Tatsunori B Hashimoto, Hugh Zhang, and Percy Liang. 2019 · 1904
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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Effective estimation of deep generative language models
Tom Pelsmaeker and Wilker Aziz. 2019 · 1904
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019b · 1904
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Training language gans from scratch
Cyprien de Masson d’Autume, Mihaela Rosca, Jack Rae, and Shakir Mohamed. 2019 · 1905
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Jiatao Gu, Changhan Wang, and Jake Zhao. 2019b · 1905
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Mass: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019a · 1905
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Paperrobot: Incremental draft generation of scientific ideas
Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, and Yi Luan. 2019b · 1905
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Kermit: Generative insertion-based modeling for sequences
William Chan, Nikita Kitaev, Kelvin Guu, Mitchell Stern, and Jakob Uszkoreit. 2019 · 1906
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An introduction to variational autoencoders
Diederik P Kingma and Max Welling. 2019 · 1906
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Rankqa: Neural question answering with answer re-ranking
Bernhard Kratzwald, Anna Eigenmann, and Stefan Feuerriegel. 2019 · 1906
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Stand-alone self-attention in vision models
Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jonathon Shlens. 2019 · 1906
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On the weaknesses of reinforcement learning for neural machine translation
Leshem Choshen, Lior Fox, Zohar Aizenbud, and Omri Abend. 2019 · 1907
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Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind Picard. 2019 · 1907
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019b · 1907
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Ernie 2.0: A continual pre-training framework for language understanding
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. 2019b · 1907
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Implicit deep latent variable models for text generation
Le Fang, Chunyuan Li, Jianfeng Gao, Wen Dong, and Changyou Chen. 2019 · 1908
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Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019b · 1908
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Transformer dissection: An unified understanding for transformer’s attention via the lens of kernel
Yao-Hung Hubert Tsai, Shaojie Bai, Makoto Yamada, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2019 · 1908
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Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019b · 1908
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Towards neural language evaluators
Hassan Kané, Yusuf Kocyigit, Pelkins Ajanoh, Ali Abdalla, and Mohamed Coulibali. 2019 · 1909
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Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019 · 1909
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Flowseq: Non-autoregressive conditional sequence generation with generative flow
Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, and Eduard Hovy. 2019b · 1909
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Gábor Melis, Tomáš Kočiskỳ, and Phil Blunsom. 2019 · 1909
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Hierarchical reinforcement learning for open-domain dialog
Abdelrhman Saleh, Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, and Rosalind Picard. 2019 · 1909
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Megatron-lm: Training multi-billion parameter language models using gpu model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. 2019 · 1909
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On extractive and abstractive neural document summarization with transformer language models
Sandeep Subramanian, Raymond Li, Jonathan Pilault, and Christopher Pal. 2019 · 1909
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Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 1909
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019 · 1909
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Controllable sentence simplification: Employing syntactic and lexical constraints
Jonathan Mallinson and Mirella Lapata. 2019 · 1910
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Controllable sentence simplification
Louis Martin, Benoît Sagot, Éric de la Clergerie, and Antoine Bordes. 2019b · 1910
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019 · 1910
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Capturing greater context for question generation
Luu Anh Tuan, Darsh J Shah, and Regina Barzilay. 2019 · 1910
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Sequence modeling with unconstrained generation order
Dmitrii Emelianenko, Elena Voita, and Pavel Serdyukov. 2019 · 1911
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How can we know what language models know?
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2019 · 1911
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Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2019 · 1911
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Commongen: A constrained text generation dataset towards generative commonsense reasoning
Bill Yuchen Lin, Ming Shen, Yu Xing, Pei Zhou, and Xiang Ren. 2019 · 1911
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Natural language generation challenges for explainable ai
Ehud Reiter. 2019 · 1911
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Natural language generation using reinforcement learning with external rewards
Vidhushini Srinivasan, Sashank Santhanam, and Samira Shaikh. 2019 · 1911
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Learning to predict explainable plots for neural story generation
Gang Chen, Yang Liu, Huanbo Luan, Meng Zhang, Qun Liu, and Maosong Sun. 2019a · 1912
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Plug and play language models: a simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2019 · 1912
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olmpics–on what language model pre-training captures
Alon Talmor, Yanai Elazar, Yoav Goldberg, and Jonathan Berant. 2019 · 1912
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A new readability yardstick
Rudolph Flesch. 1948 · 1948
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Computing machinery and intelligence-am turing
Intelligence by AM Turing. 1950 · 1950
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Binary codes capable of correcting deletions, insertions, and reversals
Vladimir I Levenshtein. 1966 · 1966
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Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel
J Peter Kincaid, Robert P Fishburne Jr, Richard L Rogers, and Brad S Chissom. 1975 · 1975
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Perplexity—a measure of the difficulty of speech recognition tasks
Fred Jelinek, Robert L Mercer, Lalit R Bahl, and James K Baker. 1977 · 1977
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How to write plain English: A book for lawyers and consumers
Rudolf Franz Flesch. 1979 · 1979
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Generating long and informative reviews with aspect-aware coarse-to-fine decoding
Junyi Li, Wayne Xin Zhao, Ji-Rong Wen, and Yang Song. 2019a · 1979
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A learning algorithm for boltzmann machines
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski. 1985 · 1985
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Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1986 · 1986
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Backpropagation through time: What it is and how to do it
P Werbos. 1989 · 1989
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A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser. 1989 · 1989
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
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Evaluating natural language processing systems
Julia Rose Galliers and K Sparck Jones. 1993 · 1993
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Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi. 1994 · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal. 1997 · 1997
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Accelerated dp based search for statistical translation
Christoph Tillmann, Stephan Vogel, Hermann Ney, Arkaitz Zubiaga, and Hassan Sawaf. 1997 · 1997
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Paradise: A framework for evaluating spoken dialogue agents
Marilyn A Walker, Diane J Litman, Candace A Kamm, and Alicia Abella. 1997 · 1997
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Simplifying natural language for aphasic readers
Siobhan Lucy Devlin. 1999 · 1999
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The tipster summac text summarization evaluation
Inderjeet Mani, David House, Gary Klein, Lynette Hirschman, Therese Firmin, and Beth M Sundheim. 1999 · 1999
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Using grice’s maxim of quantity to select the content of plan descriptions
R Michael Young. 1999 · 1999
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An evaluation tool for machine translation: Fast evaluation for mt research
Sonja Nießen, Franz Josef Och, Gregor Leusch, Hermann Ney, et al. 2000 · 2000
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A machine learning approach to the automatic evaluation of machine translation
Simon Corston-Oliver, Michael Gamon, and Chris Brockett. 2001 · 2001
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Semi-autoregressive training improves mask-predict decoding
Marjan Ghazvininejad, Omer Levy, and Luke Zettlemoyer. 2020 · 2001
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
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Rhetorically controlled encoder-decoder for modern chinese poetry generation
Zhiqiang Liu, Zuohui Fu, Jie Cao, Gerard de Melo, Yik-Cheung Tam, Cheng Niu, and Jie Zhou. 2019c · 2001
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Insertion-deletion transformer
Laura Ruis, Mitchell Stern, Julia Proskurnia, and William Chan. 2020 · 2001
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Self-adversarial learning with comparative discrimination for text generation
Wangchunshu Zhou, Tao Ge, Ke Xu, Furu Wei, and Ming Zhou. 2020 · 2001
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The diag experiments: Natural language generation for intelligent tutoring systems
Barbara Di Eugenio, Michael Glass, and Michael Trolio. 2002 · 2002
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Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
George Doddington. 2002 · 2002
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Summarization beyond sentence extraction: A probabilistic approach to sentence compression
Kevin Knight and Daniel Marcu. 2002 · 2002
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Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo. 2020 · 2002
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2002
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A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003 · 2003
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Polarized-vae: Proximity based disentangled representation learning for text generation
Vikash Balasubramanian, Ivan Kobyzev, Hareesh Bahuleyan, Ilya Shapiro, and Olga Vechtomova. 2020 · 2004
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Pretrained transformers improve out-of-distribution robustness
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, and Dawn Song. 2020 · 2004
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Hooks in the headline: Learning to generate headlines with controlled styles
Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, and Peter Szolovits. 2020 · 2004
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A learning approach to improving sentence-level mt evaluation
Alex Kulesza and Stuart Shieber. 2004 · 2004
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Orange: a method for evaluating automatic evaluation metrics for machine translation
Chin-Yew Lin and Franz Josef Och. 2004b · 2004
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Adversarial training for large neural language models
Xiaodong Liu, Hao Cheng, Pengcheng He, Weizhu Chen, Yu Wang, Hoifung Poon, and Jianfeng Gao. 2020 · 2004
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Training a sentence-level machine translation confidence measure
Christopher Quirk. 2004 · 2004
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Non-autoregressive machine translation with latent alignments
Chitwan Saharia, William Chan, Saurabh Saxena, and Mohammad Norouzi. 2020 · 2004
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Bleurt: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur P Parikh. 2020 · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
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Language (technology) is power: A critical survey of” bias” in nlp
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2005
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2005
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Enabling language models to fill in the blanks
Chris Donahue, Mina Lee, and Percy Liang. 2020 · 2005
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Sentence-level mt evaluation without reference translations: Beyond language modeling
Michael Gamon, Anthony Aue, and Martine Smets. 2005 · 2005
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Automation of summary evaluation by the pyramid method
Aaron Harnly, Ani Nenkova, Rebecca Passonneau, and Owen Rambow. 2005 · 2005
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020b · 2005
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How can we accelerate progress towards human-like linguistic generalization?
Tal Linzen. 2020 · 2005
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Learning an unreferenced metric for online dialogue evaluation
Koustuv Sinha, Prasanna Parthasarathi, Jasmine Wang, Ryan Lowe, William L Hamilton, and Joelle Pineau. 2020 · 2005
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Evaluating evaluation methods for generation in the presence of variation
Amanda Stent, Matthew Marge, and Mohit Singhai. 2005 · 2005
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Pointer: Constrained text generation via insertion-based generative pre-training
Yizhe Zhang, Guoyin Wang, Chunyuan Li, Zhe Gan, Chris Brockett, and Bill Dolan. 2020 · 2005
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Generating and evaluating evaluative arguments
Giuseppe Carenini and Johanna D Moore. 2006 · 2006
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Evaluation of text generation: A survey
Asli Celikyilmaz, Elizabeth Clark, and Jianfeng Gao. 2020 · 2006
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Testing statistical hypotheses
Erich L Lehmann and Joseph P Romano. 2006 · 2006
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Cder: Efficient mt evaluation using block movements
Gregor Leusch, Nicola Ueffing, and Hermann Ney. 2006 · 2006
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Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, and Luke Zettlemoyer. 2020a · 2006
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An information-theoretic approach to automatic evaluation of summaries
Chin-Yew Lin, Guihong Cao, Jianfeng Gao, and Jian-Yun Nie. 2006 · 2006
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Tangled up in bleu: Reevaluating the evaluation of automatic machine translation evaluation metrics
Nitika Mathur, Tim Baldwin, and Trevor Cohn. 2020 · 2006
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Syntactic simplification and text cohesion
Advaith Siddharthan. 2006 · 2006
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A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
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Supervised automatic evaluation for summarization with voted regression model
Tsutomu Hirao, Manabu Okumura, Norihito Yasuda, and Hideki Isozaki. 2007 · 2007
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Ilr-based mt comprehension test with multi-level questions
Douglas Jones, Martha Herzog, Hussny Ibrahim, Arvind Jairam, Wade Shen, Edward Gibson, and Michael Emonts. 2007 · 2007
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Do transformers need deep long-range memory
Jack W Rae and Ali Razavi. 2020 · 2007
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Knowledge-aware language model pretraining
Corby Rosset, Chenyan Xiong, Minh Phan, Xia Song, Paul Bennett, and Saurabh Tiwary. 2020 · 2007
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Facts as experts: Adaptable and interpretable neural memory over symbolic knowledge
Pat Verga, Haitian Sun, Livio Baldini Soares, and William W Cohen. 2020 · 2007
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed. 2020 · 2007
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Mind the gap: Dangers of divorcing evaluations of summary content from linguistic quality
John Conroy and Hoa Trang Dang. 2008 · 2008
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008 · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
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Evaluating effects of machine translation accuracy on cross-lingual patent retrieval
Atsushi Fujii, Masao Utiyama, Mikio Yamamoto, and Takehito Utsuro. 2009 · 2009
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Computational approaches to storytelling and creativity
Pablo Gervás. 2009 · 2009
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Speech and language processing daniel jurafsky and james h. martin (stanford university and university of colorado at boulder) pearson prentice hall, 2009, xxxi+ 988 pp; hardbound, isbn 978-0-13-187321-6, $115.00
Vlado Keselj. 2009 · 2009
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The meteor metric for automatic evaluation of machine translation
Alon Lavie and Michael J Denkowski. 2009 · 2009
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Who, what, when, where, why?: comparing multiple approaches to the cross-lingual 5w task
Kristen Parton, Kathleen R McKeown, Bob Coyne, Mona T Diab, Ralph Grishman, Dilek Hakkani-Tür, Mary Harper, Heng Ji, Wei Yun Ma, Adam Meyers, et al. 2009 · 2009
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An investigation into the validity of some metrics for automatically evaluating natural language generation systems
Ehud Reiter and Anja Belz. 2009 · 2009
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Fluency, adequacy, or hter?: exploring different human judgments with a tunable mt metric
Matthew Snover, Nitin Madnani, Bonnie J Dorr, and Richard Schwartz. 2009 · 2009
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Estimating the sentence-level quality of machine translation systems
Lucia Specia, Marco Turchi, Nicola Cancedda, Marc Dymetman, and Nello Cristianini. 2009 · 2009
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Automatic evaluation of translation quality for distant language pairs
Hideki Isozaki, Tsutomu Hirao, Kevin Duh, Katsuhito Sudoh, and Hajime Tsukada. 2010 · 2010
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A semantic and syntactic text simplification tool for health content
Sasikiran Kandula, Dorothy Curtis, and Qing Zeng-Treitler. 2010 · 2010
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Recurrent neural network based language model
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. 2010 · 2010
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Machine translation evaluation versus quality estimation
Lucia Specia, Dhwaj Raj, and Marco Turchi. 2010 · 2010
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Evaluate with confidence estimation: Machine ranking of translation outputs using grammatical features
Eleftherios Avramidis, Maja Popović, David Vilar, and Aljoscha Burchardt. 2011 · 2011
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Meant: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility based on semantic roles
Chi-kiu Lo and Dekai Wu. 2011 · 2011
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E-rating machine translation
Kristen Parton, Joel Tetreault, Nitin Madnani, and Martin Chodorow. 2011 · 2011
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Tine: A metric to assess mt adequacy
Miguel Rios, Wilker Aziz, and Lucia Specia. 2011 · 2011
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Generating text with recurrent neural networks
Ilya Sutskever, James Martens, and Geoffrey E Hinton. 2011 · 2011
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Lepor: A robust evaluation metric for machine translation with augmented factors
Aaron LF Han, Derek F Wong, and Lidia S Chao. 2012 · 2012
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Statistical language models based on neural networks
Tomáš Mikolov. 2012 · 2012
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An assessment of the accuracy of automatic evaluation in summarization
Karolina Owczarzak, John Conroy, Hoa Trang Dang, and Ani Nenkova. 2012 · 2012
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Evaluation measures for text summarization
Josef Steinberger and Karel Ježek. 2012 · 2012
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Joint learning of a dual smt system for paraphrase generation
Hong Sun and Ming Zhou. 2012 · 2012
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Text complexity and text simplification
Irina Temnikova. 2012 · 2012
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Sentence simplification by monolingual machine translation
Sander Wubben, Antal Van Den Bosch, and Emiel Krahmer. 2012 · 2012
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Text-based measures of document diversity
Kevin Bache, David Newman, and Padhraic Smyth. 2013 · 2013
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013 · 2013
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A systematic exploration of diversity in machine translation
Kevin Gimpel, Dhruv Batra, Chris Dyer, and Gregory Shakhnarovich. 2013 · 2013
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Generating sequences with recurrent neural networks
Alex Graves. 2013 · 2013
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013 · 2013
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Framing image description as a ranking task: Data, models and evaluation metrics
Micah Hodosh, Peter Young, and Julia Hockenmaier. 2013 · 2013
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Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom. 2013 · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
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Babytalk: Understanding and generating simple image descriptions
Girish Kulkarni, Visruth Premraj, Vicente Ordonez, Sagnik Dhar, Siming Li, Yejin Choi, Alexander C Berg, and Tamara L Berg. 2013 · 2013
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Automatically assessing machine summary content without a gold standard
Annie Louis and Ani Nenkova. 2013 · 2013
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A decade of automatic content evaluation of news summaries: Reassessing the state of the art
Peter A Rankel, John Conroy, Hoa Trang Dang, and Ani Nenkova. 2013 · 2013
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I, poet: automatic chinese poetry composition through a generative summarization framework under constrained optimization
Rui Yan, Han Jiang, Mirella Lapata, Shou-De Lin, Xueqiang Lv, and Xiaoming Li. 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Towards comparative evaluation and shared tasks for nlg in interactive systems
Anja Belz and Helen Hastie. 2014 · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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