A discriminative language model with pseudo-negative samples

By Daisuke Okanohara, Junichi Tsujii. 2007

57 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Natural Language Processing (Almost) from Scratch (2011, methodology): “Okanohara and Tsujii (2007) use a related approach to avoiding the entropy criteria using a binary classification approach (correct/incorrect phrase).”
Boosting Self-Supervised Learning via Knowledge Transfer (2018, background): “[24] to learn representations in the text domain.”
Stylometric Analysis of Scientific Articles (2012, methodology): “While TSG s have not been used previously in sty-lometry, Post (2011) uses them to predict sentence grammaticality (i.e. detecting pseudo-sentences following Okanohara and Tsujii (2007) and Cherry and Quirk (2008)).”
Judging Grammaticality: Experiments in Sentence Classification (2013, methodology): “The accuracy of our method is within the same range as that of Andersen (2007) and Okanohara and Tsujii (2007) but falls short of the accuracy of 81.75% reported by Sun et al. (2007).”
A unified architecture for natural language processing: deep neural networks with multitask learning (2008, methodology): “We consider a diﬀerent setting: predict whether the given sequence exists in nature, or not, following the methodology of (Okanohara & Tsujii, 2007).”
Human-like Natural Language Generation Using Monte Carlo Tree Search (2016, methodology): “Regarding this problem, Okanohara and Tsujii (2007) proposed a method to use a semi-Markov class model to identify the grammaticality of the sentence.”
Artificial error generation for translation-based grammatical error correction (2016, background): “Firstly, they seem naturally suited for error detection, since we can judge the grammaticality of a sentence or phrase from their estimated probabilities (Heilman et al., 2014; Hernandez and Calvo, 2014; Lee et al., 2014; Lin and Chen, 2015; Okanohara and Tsujii, 2007; Wagner et al., 2007).”
Contrastive Representation Learning: A Framework and Review (2020, methodology): “Following the idea proposed in [76] to learn a language model discriminatively, Collobert and Weston [21] learned a language model to perform a two-class classification task to determine whether and how the middle word of a context window is related to its context or not.”
GenERRate: Generating Errors for Use in Grammatical Error Detection (2009, background): “Examples are Andersen (2006; 2007), Okanohara and Tsujii (2007), Sun et al. (2007) and Wagner et al. (2007).”