Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
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[1] Nanyun Peng,et al. The Woman Worked as a Babysitter: On Biases in Language Generation , 2019, EMNLP.
[2] Kenneth Ward Church,et al. Word Association Norms, Mutual Information, and Lexicography , 1989, ACL.
[3] M. Haselton,et al. The effect of the promiscuity stereotype on opposition to gay rights , 2017, PloS one.
[4] Lysandre Debut,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[5] Mohit Bansal,et al. Adversarial NLI: A New Benchmark for Natural Language Understanding , 2020, ACL.
[6] Zhiyi Ma,et al. Dynabench: Rethinking Benchmarking in NLP , 2021, NAACL.
[7] Noam Shazeer,et al. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity , 2021, ArXiv.
[8] Brendan T. O'Connor,et al. Demographic Dialectal Variation in Social Media: A Case Study of African-American English , 2016, EMNLP.
[9] Samuel R. Bowman,et al. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.
[10] Omer Levy,et al. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.
[11] Hannaneh Hajishirzi,et al. UnifiedQA: Crossing Format Boundaries With a Single QA System , 2020, FINDINGS.
[12] Danqi Chen,et al. Making Pre-trained Language Models Better Few-shot Learners , 2021, ACL/IJCNLP.
[13] Gunhee Kim,et al. Abstractive Summarization of Reddit Posts with Multi-level Memory Networks , 2018, NAACL.
[14] Omer Levy,et al. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems , 2019, NeurIPS.
[15] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[16] Iryna Gurevych,et al. C4Corpus: Multilingual Web-size Corpus with Free License , 2016, LREC.
[17] Ido Dagan,et al. The Third PASCAL Recognizing Textual Entailment Challenge , 2007, ACL-PASCAL@ACL.
[18] Mirella Lapata,et al. Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization , 2018, EMNLP.
[19] Timnit Gebru,et al. Lessons from archives: strategies for collecting sociocultural data in machine learning , 2019, FAT*.
[20] Doug Downey,et al. Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks , 2020, ACL.
[21] Vishrav Chaudhary,et al. CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data , 2019, LREC.
[22] Holger Schwenk,et al. WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia , 2019, EACL.
[23] Bowen Zhou,et al. Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond , 2016, CoNLL.
[24] William Yang Wang,et al. “Love ya, jerkface”: Using Sparse Log-Linear Models to Build Positive and Impolite Relationships with Teens , 2012, SIGDIAL Conference.
[25] Amandalynne Paullada,et al. Data and its (dis)contents: A survey of dataset development and use in machine learning research , 2020, Patterns.
[26] Ali Farhadi,et al. Defending Against Neural Fake News , 2019, NeurIPS.
[27] Emily M. Bender,et al. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science , 2018, TACL.
[28] Jian Zhang,et al. SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.
[29] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[30] Daniel Khashabi,et al. UNQOVERing Stereotypical Biases via Underspecified Questions , 2020, FINDINGS.
[31] Philipp Koehn,et al. A Massive Collection of Cross-Lingual Web-Document Pairs , 2019, EMNLP.
[32] Sanja Fidler,et al. Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[33] Adam D. Galinsky,et al. The Reappropriation of Stigmatizing Labels: The Reciprocal Relationship Between Power and Self-Labeling , 2013 .
[34] Roland Schäfer,et al. CommonCOW: Massively Huge Web Corpora from CommonCrawl Data and a Method to Distribute them Freely under Restrictive EU Copyright Laws , 2016, LREC.
[35] Ido Dagan,et al. The Sixth PASCAL Recognizing Textual Entailment Challenge , 2009, TAC.
[36] Mark Chen,et al. Scaling Laws for Autoregressive Generative Modeling , 2020, ArXiv.
[37] Ankur Bapna,et al. Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets , 2021, TACL.
[38] Emily Ahn,et al. Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts , 2019, EMNLP.
[39] Emily Denton,et al. Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure , 2020, FAccT.
[40] Christopher Potts,et al. Learning Word Vectors for Sentiment Analysis , 2011, ACL.
[41] Samuel R. Bowman,et al. Neural Network Acceptability Judgments , 2018, Transactions of the Association for Computational Linguistics.
[42] Charles Foster,et al. The Pile: An 800GB Dataset of Diverse Text for Language Modeling , 2020, ArXiv.
[43] Jure Leskovec,et al. Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora , 2016, EMNLP.
[44] Timnit Gebru,et al. Datasheets for datasets , 2018, Commun. ACM.
[45] Chris Brockett,et al. Automatically Constructing a Corpus of Sentential Paraphrases , 2005, IJCNLP.
[46] Emily M. Bender,et al. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 , 2021, FAccT.
[47] Hady Elsahar,et al. Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages , 2020, FINDINGS.
[48] Eric Gilbert,et al. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.
[49] Ming-Wei Chang,et al. BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions , 2019, NAACL.
[50] Colin Raffel,et al. Extracting Training Data from Large Language Models , 2020, USENIX Security Symposium.
[51] Roy Bar-Haim,et al. The Second PASCAL Recognising Textual Entailment Challenge , 2006 .
[52] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[53] Quoc V. Le,et al. A Simple Method for Commonsense Reasoning , 2018, ArXiv.
[54] David Grangier,et al. Neural Text Generation from Structured Data with Application to the Biography Domain , 2016, EMNLP.
[55] Laurent Romary,et al. A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages , 2020, ACL.
[56] Sanjoy Dasgupta,et al. A Non-Parametric Test to Detect Data-Copying in Generative Models , 2020, ArXiv.
[57] Alec Radford,et al. Scaling Laws for Neural Language Models , 2020, ArXiv.
[58] Kenneth Heafield,et al. ParaCrawl: Web-Scale Acquisition of Parallel Corpora , 2020, ACL.
[59] Ido Dagan,et al. The Third PASCAL Recognizing Textual Entailment Challenge , 2007, ACL-PASCAL@ACL.
[60] Jason Weston,et al. Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack , 2019, EMNLP.
[61] J. Rosa,et al. Looking like a Language, Sounding like a Race , 2018 .
[62] Alexandra Luccioni,et al. What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus , 2021, ACL.
[63] Colin Raffel,et al. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer , 2021, NAACL.
[64] Eneko Agirre,et al. SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation , 2017, *SEMEVAL.
[65] Phil Blunsom,et al. Teaching Machines to Read and Comprehend , 2015, NIPS.
[66] Christopher Potts,et al. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.
[67] Hector J. Levesque,et al. The Winograd Schema Challenge , 2011, AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning.
[68] Yejin Choi,et al. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models , 2020, FINDINGS.
[69] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[70] Solon Barocas,et al. Language (Technology) is Power: A Critical Survey of “Bias” in NLP , 2020, ACL.
[71] Adam M. Croom. How to do things with slurs: Studies in the way of derogatory words , 2013 .