Fetching the paper…
Reading the bibliography…
Language style is often used by writers to convey their intentions, identities, and mastery of language.
A lexical, syntactic, and semantic perspective for understanding style in text
Gaurav Verma and Balaji Vasan Srinivasan. 2019 · 1909
Earlier work this paper cites.
Zero-shot text classification with generative language models
Raul Puri and Bryan Catanzaro. 2019 · 1912
Earlier work this paper cites.
Generating natural language under pragmatic constraints
Eduard Hovy. 1987 · 1987
Earlier work this paper cites.
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
Earlier work this paper cites.
Reliability in content analysis: Some common misconceptions and recommendations
Klaus Krippendorff. 2004 · 2004
Earlier work this paper cites.
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
Earlier work this paper cites.
Useful resources: Offensive/profane word list
Luis Von Ahn. 2005 · 2005
Earlier work this paper cites.
Recognizing contextual polarity in phrase-level sentiment analysis
Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005 · 2005
Earlier work this paper cites.
Inter-coder agreement for computational linguistics
Ron Artstein and Massimo Poesio. 2008 · 2008
Earlier work this paper cites.
Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon
Saif Mohammad and Peter Turney. 2010 · 2010
Earlier work this paper cites.
The psychological meaning of words: Liwc and computerized text analysis methods
Yla R Tausczik and James W Pennebaker. 2010 · 2010
Earlier work this paper cites.
Inducing lexicons of formality from corpora
Tong Wang, Julian Brooke, and Graeme Hirst. 2010 · 2010
Earlier work this paper cites.
Lexicon-based methods for sentiment analysis
Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011 · 2011
Earlier work this paper cites.
Paraphrasing for style
Wei Xu, Alan Ritter, Bill Dolan, Ralph Grishman, and Colin Cherry. 2012 · 2012
Earlier work this paper cites.
A computational approach to politeness with application to social factors
Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets
Saif Mohammad, Svetlana Kiritchenko, and Xiaodan Zhu. 2013 · 2013
Earlier work this paper cites.
Measuring ideological proportions in political speeches
Yanchuan Sim, Brice D. L. Acree, Justin H. Gross, and Noah A. Smith. 2013 · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Human language reveals a universal positivity bias
Peter Sheridan Dodds, Eric M Clark, Suma Desu, Morgan R Frank, Andrew J Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M Kloumann, James P Bagrow, et al. 2015 · 2015
Earlier work this paper cites.
Context-sensitive lexicon features for neural sentiment analysis
Zhiyang Teng, Duy-Tin Vo, and Yue Zhang. 2016 · 2016
Cited alongside, same era.
Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017 · 2017
Cited alongside, same era.
Unsupervised learning for lexicon-based classification
Jacob Eisenstein. 2017 · 2017
Cited alongside, same era.
From shakespeare to Twitter: What are language styles all about?
Wei Xu. 2017 · 2017
Cited alongside, same era.
A large self-annotated corpus for sarcasm
Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli. 2018 · 2018
Cited alongside, same era.
A word-complexity lexicon and a neural readability ranking model for lexical simplification
Mounica Maddela and Wei Xu. 2018 · 2018
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Later among the works it cites.
Detecting domain polarity-changes of words in a sentiment lexicon
Shuai Wang, Guangyi Lv, Sahisnu Mazumder, and Bing Liu. 2021 · 2021
Later among the works it cites.
Calibrate before use: Improving few-shot performance of language models
Tony Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Later among the works it cites.
Adapting language models for zero-shot learning by meta-tuning on dataset and prompt collections
Ruiqi Zhong, Kristy Lee, Zheng Zhang, and Dan Klein. 2021 · 2021
Later among the works it cites.
Controlled text generation with natural language instructions
Wangchunshu Zhou, Yuchen Eleanor Jiang, Ethan Wilcox, Ryan Cotterell, and Mrinmaya Sachan. 2023 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Short Text Corpus For Humor Detection
CrowdTruth. 2016 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
Cited alongside, same era.
Text classification using label names only: A language model self-training approach
Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, and Jiawei Han. 2020 · 2020
Cited alongside, same era.
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. 2020 · 2020
Cited alongside, same era.
CEFR-based sentence difficulty annotation and assessment
Yuki Arase, Satoru Uchida, and Tomoyuki Kajiwara. 2022 · 2022
Later among the works it cites.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Later among the works it cites.
Deep Learning for Text Style Transfer: A Survey
Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. 2022 · 2022
Later among the works it cites.
What makes good in-context examples for GPT-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022
Later among the works it cites.
MetaICL: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022a · 2022
Later among the works it cites.
Description-driven task-oriented dialog modeling
Jeffrey Zhao, Raghav Gupta, Yuan Cao, Dian Yu, Mingqiu Wang, Harrison Lee, Abhinav Rastogi, Izhak Shafran, and Yonghui Wu. 2022 · 2022
Later among the works it cites.
The case for 4-bit precision: k-bit inference scaling laws
Tim Dettmers and Luke Zettlemoyer. 2023 · 2023
Closest in time.
The benefits of label-description training for zero-shot text classification
Lingyu Gao, Debanjan Ghosh, and Kevin Gimpel. 2023 · 2023
Closest in time.
Self-specialization: Uncovering latent expertise within large language models
Junmo Kang, Hongyin Luo, Yada Zhu, James Glass, David Cox, Alan Ritter, Rogerio Feris, and Leonid Karlinsky. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Closest in time.
Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
Closest in time.
Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al. 2023 · 2023
Closest in time.