Learning to predict script events from domain-specific text
By Rachel Rudinger, Vera Demberg, Ashutosh Modi, Benjamin Van Durme, Manfred Pinkal. 2015
25 citations recorded by the source corpus.
The automatic induction of scripts (Schank and Abelson, 1977) has been the focus of many recent works. In this paper, we employ a variety of these methods to learn Schank and Abelson’s canonical restaurant script, using a novel dataset of restaurant narratives we have compiled from a website called “Dinners from Hell.” Our models learn narrative chains, script-like structures that we evaluate with the “narrative cloze” task (Chambers and Jurafsky, 2008).
Subfields: Artificial Intelligence, Information Systems, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yejin Choi
- Rowan Zellers
- Mohit Bansal
- Luke S. Zettlemoyer
- Ali Farhadi
- Ari Holtzman
- Noah A. Smith
- Jianfeng Gao
In Information Systems
- Ali Farhadi
- Jan Buys
- Yejin Choi
- Stefan Thater
- Karl Pichotta
- Rowan Zellers
- Mohit Bansal
- Richard Socher
In Natural Language Processing
- Yejin Choi
- Rowan Zellers
- Ari Holtzman
- Ali Farhadi
- Luke S. Zettlemoyer
- Mohit Bansal
- Yonatan Bisk
- Hannaneh Hajishirzi
Citations in the corpus
- HellaSwag: Can a Machine Really Finish Your Sentence? (2019, background): “3These biases simply inflate model performance, but past work has also shown that are unwanted social biases induced when humans write the endings, in terms of gender and race (Rudinger et al., 2015).”
- Benchmarking Hierarchical Script Knowledge (2019, background): “Many script learning systems are based on event co-occurrence and language modeling in large text corpora, and can infer implicit events without creating explicit situation-specific frame structures (Chambers and Jurafsky, 2008; Rudinger et al., 2015; Pichotta and Mooney, 2016).”
- SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge (2018, methodology): “The Dinners from Hell corpus (Rudinger et al., 2015a) is a similar dataset centered around the EATING IN A RESTAURANT scenario.”