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There has been growing interest in automatically predicting missing type annotations in programs written in Python and JavaScript.
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 · 1910
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A new algorithm for data compression
Philip Gage · 1994
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Predicting Structured Data
G. BakIr, Neural Information Processing Systems Foundation, T. Hofmann, A.J. Smola, B. Schölkopf, and B. Taskar · 2007
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Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Gautier Izacard and Edouard Grave · 2007
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Gradual typing for objects
Jeremy Siek and Walid Taha · 2007
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Search-based structured prediction
Hal Daumé, John Langford, and Daniel Marcu · 2009
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Autoregressive entity retrieval
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Japanese and korean voice search
Mike Schuster and Kaisuke Nakajima · 2012
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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Predicting program properties from” big code”
Veselin Raychev, Martin Vechev, and Andreas Krause · 2015
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
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Reading Wikipedia to Answer Open-Domain Questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
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Deep learning type inference
Vincent J Hellendoorn, Christian Bird, Earl T Barr, and Miltiadis Allamanis · 2018
Cited alongside, same era.
Assessing the type annotation burden
John-Paul Ore, Sebastian Elbaum, Carrick Detweiler, and Lambros Karkazis · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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The adverse effects of code duplication in machine learning models of code
Miltiadis Allamanis · 2019
Cited alongside, same era.
Nl2type: inferring javascript function types from natural language information
Rabee Sohail Malik, Jibesh Patra, and Michael Pradel · 2019
Cited alongside, same era.
Typilus: Neural type hints
Miltiadis Allamanis, Earl T Barr, Soline Ducousso, and Zheng Gao · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Unified pre-training for program understanding and generation
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack W. Rae, Erich Elsen, and Laurent Sifre · 2021
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Learning type annotation: is big data enough?
Kevin Jesse, Premkumar T Devanbu, and Toufique Ahmed · 2021
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REALM: Retrieval-Augmented Language Model Pre-Training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang · 2020
Cited alongside, same era.
Dense Passage Retrieval for Open-Domain Question Answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
Cited alongside, same era.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela · 2020
Cited alongside, same era.
Opttyper: Probabilistic type inference by optimising logical and natural constraints
Irene Vlassi Pandi, Earl T Barr, Andrew D Gordon, and Charles Sutton · 2020
Cited alongside, same era.
Learning to update natural language comments based on code changes
Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, and Raymond Mooney · 2020
Cited alongside, same era.
Typewriter: Neural type prediction with search-based validation
Michael Pradel, Georgios Gousios, Jason Liu, and Satish Chandra · 2020
Cited alongside, same era.
Manytypes4py: A benchmark python dataset for machine learning-based type inference
A. M. Mir, E. Latoskinas, and G. Gousios · 2021
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WebGPT: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman · 2021
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KILT: a Benchmark for Knowledge Intensive Language Tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel · 2021
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Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
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Cross-domain evaluation of a deep learning-based type inference system
Bernd Gruner, Tim Sonnekalb, Thomas S Heinze, and Clemens-Alexander Brust · 2022
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Learning to predict user-defined types
Kevin Jesse, Premkumar Devanbu, and Anand Ashok Sawant · 2022
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Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
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Type4py: practical deep similarity learning-based type inference for python
Amir M Mir, Evaldas Latoškinas, Sebastian Proksch, and Georgios Gousios · 2022
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Static inference meets deep learning: A hybrid type inference approach for python
Yun Peng, Cuiyun Gao, Zongjie Li, Bowei Gao, David Lo, Qirun Zhang, and Michael Lyu · 2022
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