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Training learnable metrics using modern language models has recently emerged as a promising method for the automatic evaluation of machine translation.
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. 2019 · 1907
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Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel
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Reliability in content analysis: Some common misconceptions and recommendations
Klaus Krippendorff. 2004 · 2004
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Minimum Bayes-risk decoding for statistical machine translation
Shankar Kumar and William Byrne. 2004 · 2004
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Discriminative reranking for machine translation
Libin Shen, Anoop Sarkar, and Franz Josef Och. 2004 · 2004
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Minimum risk annealing for training log-linear models
David A. Smith and Jason Eisner. 2006 · 2006
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METEOR: An automatic metric for MT evaluation with high levels of correlation with human judgments
Alon Lavie and Abhaya Agarwal. 2007 · 2007
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A monolingual tree-based translation model for sentence simplification
Zhemin Zhu, Delphine Bernhard, and Iryna Gurevych. 2010 · 2010
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Tuning as ranking
Mark Hopkins and Jonathan May. 2011 · 2011
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Computing krippendorff’s alpha-reliability
Klaus Krippendorff. 2011 · 2011
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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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Paraphrasing for style
Wei Xu, Alan Ritter, Bill Dolan, Ralph Grishman, and Colin Cherry. 2012 · 2012
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Continuous measurement scales in human evaluation of machine translation
Yvette Graham, Timothy Baldwin, Alistair Moffat, and Justin Zobel. 2013 · 2013
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Improving text simplification language modeling using unsimplified text data
David Kauchak. 2013 · 2013
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Hybrid simplification using deep semantics and machine translation
Shashi Narayan and Claire Gardent. 2014 · 2014
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015 · 2015
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Problems in current text simplification research: New data can help
Wei Xu, Chris Callison-Burch, and Courtney Napoles. 2015 · 2015
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Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. 2016 · 2016
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Controlling politeness in neural machine translation via side constraints
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Minimum risk training for neural machine translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 2016
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Optimizing statistical machine translation for text simplification
Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 2016 · 2016
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Results of the WMT17 metrics shared task
Ondřej Bojar, Yvette Graham, and Amir Kamran. 2017 · 2017
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Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017 · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Automatic text simplification
Horacio Saggion. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Sentence simplification with deep reinforcement learning
Xingxing Zhang and Mirella Lapata. 2017 · 2017
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Unsupervised neural machine translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho. 2018 · 2018
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Results of the WMT18 metrics shared task: Both characters and embeddings achieve good performance
Qingsong Ma, Ondřej Bojar, and Yvette Graham. 2018 · 2018
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RankME: Reliable human ratings for natural language generation
Jekaterina Novikova, Ondřej Dušek, and Verena Rieser. 2018 · 2018
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Style transfer through back-translation
Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, and Alan W Black. 2018 · 2018
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Dear sir or madam, may I introduce the GYAFC dataset: Corpus, benchmarks and metrics for formality style transfer
Sudha Rao and Joel Tetreault. 2018 · 2018
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COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
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Leveraging pre-trained checkpoints for sequence generation tasks
Sascha Rothe, Shashi Narayan, and Aliaksei Severyn. 2020 · 2020
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Masked language model scoring
Julian Salazar, Davis Liang, Toan Q. Nguyen, and Katrin Kirchhoff. 2020 · 2020
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BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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BERTScore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Findings of the 2021 conference on machine translation (WMT21)
Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ondřej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-jussa, Cristina España-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin, and Marcos Zampieri. 2021 · 2021
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Semantic structural evaluation for text simplification
Elior Sulem. 2018 · 2018
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Simple and effective text simplification using semantic and neural methods
Elior Sulem, Omri Abend, and Ari Rappoport. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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EditNTS: An neural programmer-interpreter model for sentence simplification through explicit editing
Yue Dong, Zichao Li, Mehdi Rezagholizadeh, and Jackie Chi Kit Cheung. 2019 · 2019
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Reinforcement learning based text style transfer without parallel training corpus
Hongyu Gong, Suma Bhat, Lingfei Wu, JinJun Xiong, and Wen-mei Hwu. 2019 · 2019
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Results of the WMT19 metrics shared task: Segment-level and strong MT systems pose big challenges
Qingsong Ma, Johnny Wei, Ondřej Bojar, and Yvette Graham. 2019 · 2019
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Evaluating style transfer for text
Remi Mir, Bjarke Felbo, Nick Obradovich, and Iyad Rahwan. 2019 · 2019
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The (un)suitability of automatic evaluation metrics for text simplification
Fernando Alva-Manchego, Carolina Scarton, and Lucia Specia. 2021 · 2021
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Energy-based reranking: Improving neural machine translation using energy-based models
Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, and Andrew McCallum. 2021 · 2021
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Decontextualization: Making sentences stand-alone
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A statistical analysis of summarization evaluation metrics using resampling methods
Daniel Deutsch, Rotem Dror, and Dan Roth. 2021 · 2021
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BiSECT: Learning to split and rephrase sentences with bitexts
Joongwon Kim, Mounica Maddela, Reno Kriz, Wei Xu, and Chris Callison-Burch. 2021 · 2021
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Neural semi-markov CRF for monolingual word alignment
Wuwei Lan, Chao Jiang, and Wei Xu. 2021 · 2021
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Discriminative reranking for neural machine translation
Ann Lee, Michael Auli, and Marc’Aurelio Ranzato. 2021 · 2021
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Controllable text simplification with explicit paraphrasing
Mounica Maddela, Fernando Alva-Manchego, and Wei Xu. 2021 · 2021
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Controllable sentence simplification with a unified text-to-text transfer transformer
Kim Cheng Sheang and Horacio Saggion. 2021 · 2021
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Identifying weaknesses in machine translation metrics through minimum Bayes risk decoding: A case study for COMET
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Scaling instruction-finetuned language models
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Understanding iterative revision from human-written text
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Quality-aware decoding for neural machine translation
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High quality rather than high model probability: Minimum Bayes risk decoding with neural metrics
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News summarization and evaluation in the era of GPT-3
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Towards fine-grained text sentiment transfer
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MUSS: Multilingual unsupervised sentence simplification by mining paraphrases
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Training language models to follow instructions with human feedback
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