Learning Script Knowledge with Web Experiments

By Michaela Regneri, Alexander Koller, Manfred Pinkal. 2010

157 citations recorded by the source corpus.

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Citations in the corpus

A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories (2016, background): “Several expansions have since been proposed, including narrative schemas (Cham-bers and Jurafsky, 2009), script sequences (Regneri et al., 2010), and relgrams (Balasubramanian et al., 2013).”
Unsupervised Learning from Narrated Instruction Videos (2015, background): “Similarly to R n , we allow the possibility that a whole row of Z n is zero, indicating that no step is visually present for the corresponding time interval.”
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge (2018, methodology): “As a starting point for our data collection, we use 1 www.mturk.com scenarios from three script data collections (Regneri et al., 2010; Singh et al., 2002; Wanzare et al., 2016).”
Unsupervised Learning of Evolving Relationships Between Literary Characters (2017, methodology): “Previous methods have also analyzed narratives from the perspective of events occurring in them and include scripts (Schank and Abelson 1977; 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),…”
Skip N-grams and Ranking Functions for Predicting Script Events (2012, background): “…sub-task: given an event or partial chain of events, predict other events that belong to the same script (Chambers and Jurafsky, 2008; Chambers and Jurafsky, 2009; Chambers and Ju-rafsky, 2011; Manshadi et al., 2008; McIntyre and Lapata, 2009; McIntyre and Lapata, 2010; Regneri et al., 2010).”
A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge (2014, methodology): “der of events in their descriptions (Regneri et al., 2010)).”
Learning to predict script events from domain-specific text (2015, background): “Regneri et al. (2010) attempt to learn the structure of speciﬁc scripts by eliciting event sequence descriptions (ESDs) from humans to which they apply multiple sequence alignment (MSA) to yield one global structure per script.”
Statistical Script Learning with Multi-Argument Events (2014, methodology): “Regneri et al. (2010) and Li et al. (2012) give methods for using crowdsourcing to create situation-specific scripts.”
Story Comprehension for Predicting What Happens Next (2017, background): “Such meth-ods for script-learning also include Bayesian approaches (Bejan, 2008; Frermann et al., 2014), sequence alignment algorithms (Regneri et al., 2010) and neural networks (Modi and Titov, 2014; Granroth-Wilding and Clark, 2016; Pi-chotta and Mooney, 2016).”
Learning Latent Personas of Film Characters (2013, background): “…of events by which a story is defined; in this tradition we might situate seminal work on learning procedural scripts (Schank and Abelson, 1977; Regneri et al., 2010), narrative chains (Chambers and Jurafsky, 2008), and plot structure (Finlayson, 2011; Elsner, 2012; McIntyre and Lapata, 2010;…”
Generative Event Schema Induction with Entity Disambiguation (2015, methodology): “, 2008), event sequence alignment (Regneri et al., 2010) or LDA-based approach relying on FrameNet-like semantic frames (Bejan, 2008).”
Script Induction as Language Modeling (2015, methodology): “(Chambers, 2013; Cheung et al., 2013; Cheung and Penn, 2013; Frermann et al., 2014; Manshadi et al., 2008; Modi and Titov, 2014; Regneri et al., 2010) inantly as a method for evaluating script knowledge, is more productively thought of simply as a language modeling task.”
SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge (2018, methodology): “Thus, we provided several existing collections of script data together with the machine comprehension corpus: DeScript (Wanzare et al., 2016), RKP (Regneri et al., 2010) and the OMCS stories (Singh et al., 2002).”
Learning from Narrated Instruction Videos (2015, methodology): “Hence, following [34], we formulate the problem of clustering the input transcripts as a multiple sequence align-”
Script Data for Attribute-Based Recognition of Composite Activities (2012, methodology): “Written event sequences for a scenario can be collected on a large scale using crowdsourcing [29].”
Story Ending Selection by Finding Hints From Pairwise Candidate Endings (2019, background): “Narrative chains [12], narrative schemas [13], script sequences [14], and relgrams [15] are proposed to learning narrative/event representations.”