Building a Large Annotated Corpus of English: The Penn Treebank

By Mitchell P. Marcus, Beatrice Santorini, Mary Ann Marcinkiewicz. 1993

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Attention is All you Need (2017, methodology): “We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [25], about 40K training sentences.”
Exploring the Limits of Language Modeling (2016, background): “This data set is much larger than PTB (one thousand fold, 800k word vocabulary and 1B words training data) and far more challenging.”
Generating Sequences With Recurrent Neural Networks (2013, methodology): “The first set of text prediction experiments focused on the Penn Treebank portion of the Wall Street Journal corpus [22].”
Semi-Supervised Sequence Modeling with Cross-View Training (2018, methodology): “Part-of-Speech (POS) Tagging: We use the Wall Street Journal portion of the Penn Treebank (Marcus et al., 1993).”
Dissecting Contextual Word Embeddings: Architecture and Representation (2018, methodology): “The Penn Treebank (Marcus et al., 1993) contains phrase structure annotation for approximately 40k sentences sourced from the Wall Street Journal.”
Deep Contextualized Word Representations (2018, methodology): “POS tagging To examine whether the biLM captures basic syntax, we used the context representations as input to a linear classifier that predicts POS tags with the Wall Street Journal portion of the Penn Treebank (PTB) (Marcus et al., 1993).”
MaskGAN: Better Text Generation via Filling in the ______ (2018, methodology): “The Penn Treebank dataset (Marcus et al., 1993) has a vocabulary of 10,000 unique words.”
SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016, background): “Historically, large, realistic datasets have played a critical role for driving ﬁelds forward—famous examples include ImageNet for object recognition (Deng et al., 2009) and the Penn Treebank for syntactic parsing (Marcus et al., 1993).”
Experience Grounds Language (2020, background): “The Brown Corpus (Francis, 1964) and Penn Treebank (Marcus et al., 1993) were colossal undertakings that de-ﬁned linguistic context for decades.”
A Call for Clarity in Reporting BLEU Scores (2018, background): “10 This has facilitated almost thirty years of comparisons on treebanks such as the Wall Street Journal portion of the Penn Treebank (Marcus et al., 1993).”
Matching Networks for One Shot Learning (2016, methodology): “We ran one-shot experiments on three data sets: two image classification sets (Omniglot [14] and ImageNet [19, ILSVRC-2012]) and one language modeling (Penn Treebank).”
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (2018, methodology): “These include image-caption matching with MSCOCO (Lin et al., 2014), following Kiela et al. (2018); the small-to-medium-data textunderstanding tasks collected in NLI format by Poliak et al. (2018); ordinal common sense inference (Zhang et al., 2017); POS tagging on the Penn Treebank (Marcus et al., ”
Pointer Sentinel Mixture Models (2016, methodology): “In order to compare our model to the many recent neural language models, we conduct word-level prediction experiments on the Penn Treebank (PTB) dataset (Marcus et al., 1993), pre-processed by Mikolov et al. (2010).”
An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models (2023, methodology): “We measure the perplexity of student models on language modeling benchmarks including WikiText-2, WikiText-103 (Merity et al., 2016), Lambada (Paperno et al., 2016), and the Penn Treebank (Marcus et al., 1993) (Appendix A.6, Table 15).”