CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures

By Nasrin Mostafazadeh, Alyson Grealish, Nathanael Chambers, James F. Allen, Lucy Vanderwende. 2016

145 citations recorded by the source corpus.

Learning commonsense causal and temporal relation between events is one of the major steps towards deeper language understanding. This is even more crucial for understanding stories and script learning. A prerequisite for learning scripts is a semantic framework which enables capturing rich event structures. In this paper we introduce a novel semantic annotation framework, called Causal and Temporal Relation Scheme (CaTeRS), which is unique in simultaneously capturing a comprehensive set of temporal and causal relations between events. By annotating a total of 1,600 sentences in the context of 320 five-sentence short stories sampled from ROCStories corpus, we demonstrate that these stories are indeed full of causal and temporal relations. Furthermore, we show that the CaTeRS annotation scheme enables high inter-annotator agreement for broad-coverage event entity annotation and moderate agreement on semantic link annotation.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing, Information Extraction, Computational Linguistics & Semantics

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Citations in the corpus

ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, methodology): “…temporal reasoning tasks as shown in Figure 1: event relation extraction (ERE) (Chambers et al., 2014; Ning et al., 2018; O’Gorman et al., 2016; Mostafazadeh et al., 2016) that predicts temporal relations between a pair of events, and machine reading comprehension (MRC) (Ning et al., 2020;…”
Plan-And-Write: Towards Better Automatic Storytelling (2018, background): “There is a line of research focusing on representing story event structures (Mostafazadeh et al. 2016b; McDowell et al. 2017).”
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation (2017, methodology): “Mostafazadeh et al. (2016a) introduce the task of visual question generation (VQG), in which the sys-tem itself outputs questions about the image.”
Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding (2019, methodology): “Modeling event temporal relations is crucial for story/narrative understanding and storytelling, because a story is typically composed of a sequence of events (Mostafazadeh et al., 2016a).”
Strategies for Structuring Story Generation (2019, background): “Recent work extended this to modelling story events (Martin et al., 2017; Mostafazadeh et al., 2016), plot graphs (Li et al.”
Joint Reasoning for Temporal and Causal Relations (2018, methodology): “Another dataset with parallel annotations is CaTeRs (Mostafazadeh et al., 2016b), which was primarily designed for the Story Cloze Test (Mostafazadeh et al.”
A Multi-Axis Annotation Scheme for Event Temporal Relations (2018, methodology): “, getting agreement on which event pairs should have a relation, and then what that relation should be) (Mostafazadeh et al., 2016; O’Gorman et al., 2016).”
Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation (2016, methodology): “Causation has often been divided into CAUSE, EN-ABLE and PREVENT, as outlined in Hobbs (2005) and Wolff (2007), and implemented in Mirza et al. (2014) and Mostafazadeh et al. (2016).”
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling (2020, unknown): “Scripts (Schank and Abelson, 1977) encode frequently recurring event sequences, where events are ordered by temporal relation (Chambers and Ju-rafsky, 2009), causal relation (Mostafazadeh et al., 2016b), or narrative order (Jans et al., 2012).”