Unsupervised Learning of Narrative Event Chains

By Nathanael Chambers, Dan Jurafsky. 2008

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Citations in the corpus

A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories (2016, methodology): “A commonly used evaluation is the ‘Narra-tive Cloze Test’ (Chambers and Jurafsky, 2008) in which a system predicts a held-out event (a verb and its arguments) given a set of observed events.”
OPT: Open Pre-trained Transformer Language Models (2022, background): “Prompting itself has a long history: cloze evaluations go back several decades (Chambers and Jurafsky, 2008; Mostafazadeh et al., 2016).”
Unsupervised Learning from Narrated Instruction Videos (2015, background): “Particularly related to ours is the work on learning script-like knowledge from natural language descriptions [6, 11, 26].”
DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension (2019, methodology): “A promising direction to solve this problem could be the construction of speaker-focused event chains (Chambers and Jurafsky, 2008) and dialogue-speciﬁc coreference resolution systems for more reliable evidence collection in a dialogue.”
Ordinal Common-sense Inference (2016, neutral): “The Narrative Cloze task (Chambers and Jurafsky, 2008) requires a system to score candidate inferences as to how likely they are to appear in a document that also included the provided context.”
Unsupervised Learning of Narrative Schemas and their Participants (2009, methodology): “The Chambers and Jurafsky (2008) narrative chains do not specify what type of argument ﬁlls the role of protagonist.”
Enabling Language Models to Fill in the Blanks (2020, background): “Cloze has been extended in the forms of discourse (Deyes, 1984) and narrative cloze (Chambers and Jurafsky, 2008), which remove phrases and narrative events respectively.”
Dynamic Entity Representations in Neural Language Models (2017, methodology): “This task is also closely related to the “narrative cloze” task of Chambers and Jurafsky (2008) and the “story cloze test” of Mostafazadeh et al. (2016).”
Crisp Boundary Detection Using Pointwise Mutual Information (2014, background): “PMI underlies many experiments in computational linguistics [25,26] to learn word associations (pairs of words that are likely to occur together), and recently has been used for improving image categorization [27].”
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge (2018, background): “…(Modi and Titov, 2014a; Modi and Titov, 2014b), event paraphrasing (Regneri et al., 2010; Wanzare et al., 2017) or event prediction (namely, the narrative cloze task (Chambers and Jurafsky, 2008; Chambers and Jurafsky, 2009; Pichotta and Mooney, 2014; Pichotta and Mooney, 2016; Modi, 2016)).”
Generative Event Schema Induction with Entity Disambiguation (2015, background): “Finally, Chambers and Jurafsky (2008), Chambers and Jurafsky (2009), Chambers and Ju-rafsky (2011), improved by Balasubramanian et al. (2013), and Chambers (2013) focused speciﬁcally on the induction of event roles and the identiﬁcation of chains of events for building representations from texts by…”
Say Anything: A Massively Collaborative Open Domain Story Writing Companion (2008, background): “This is not an easy problem to solve but solutions may include event prediction and modeling [16][17][18] and integrating coreference”
Guided Generation of Cause and Effect (2020, background): “Many works have followed this strategy, e.g. [ Chambers and Jurafsky, 2008; Riaz and Girju, 2010; Gordon et al. , 2011; Do et al. , 2011; Luo et al. , 2016 ] .”
Probabilistic Frame Induction (2013, background): “In terms of aim and capability, ProFinder is most similar to Chambers and Jurafsky (2011), which culminated from a series of work for identifying correlated events and arguments in narrative (Chambers and Jurafsky, 2008; Chambers and Ju-rafsky, 2009).”
CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures (2016, background): “Given that developing hand-built scripts is extremely time-consuming, there is a serious need for automatically induced scripts (Chambers and Jurafsky, 2008; Chambers and Jurafsky, 2009; Balasubramanian et al., 2013; Cheung et al., 2013; Nguyen et al., 2015).”
Statistical Script Learning with Multi-Argument Events (2014, methodology): “…cooccurEvs in line 5 takes a document D and returns all ordered pairs of co-occurring events in D, where, following the 2-skip bigram model of Jans et al. (2012), and similar to Balasubramanian et al. (2012; 2013), two events a1 and a2 are said to co-occur if they occur in order, in the same…”
Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships (2016, background): “such descriptors have been the focus of much previous work (Schank and Abelson, 1977; Chambers and Jurafsky, 2008; Chambers and Jurafsky, 2009; Orr et al., 2014).”
Unsupervised Learning of Evolving Relationships Between Literary Characters (2017, background): “…Regneri, Koller, and Pinkal 2010; Orr et al. 2014; Pichotta and Mooney 2016), plot units (Lehnert 1981; Goyal, Riloff, and Daumé III 2010; Finlayson 2012), temporal event chains (Chambers and Jurafsky 2008; 2009; Chambers 2013), bags of related events (Cheung, Poon, and Vanderwende 2013), etc.”
A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge (2014, background): “(Chambers and Jurafsky, 2008)), as not all types of scripts are elaborated in natural text – being left implicit because of assumed readers’ world knowledge.”
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task (2018, background): “Most previous studies on the subject of common sense story understanding mainly focus on generating guesses for a missing event, such as matching explicit information in a given context (Chambers and Jurafsky, 2008), paying attention to specific types of common sense knowledge, like event schema (Ch”