Modeling Naive Psychology of Characters in Simple Commonsense Stories

By Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight, Yejin Choi. 2018

113 citations recorded by the source corpus.

Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people’s mental states — a capability that is trivial for humans but remarkably hard for machines. To facilitate research addressing this challenge, we introduce a new annotation framework to explain naive psychology of story characters as fully-specified chains of mental states with respect to motivations and emotional reactions. Our work presents a new large-scale dataset with rich low-level annotations and establishes baseline performance on several new tasks, suggesting avenues for future research.

Subfields: Artificial Intelligence, 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

In Natural Language Processing

Citations in the corpus

ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension (2018, background): “There are other datasets (Roemmele et al., 2011; Zhang et al., 2017; Rashkin et al., 2018a,b) targeting different aspects of commonsense reasoning.”
Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs (2022, background): “…seems to be crucial for LLMs (Kaplan et al., 2020; Chowd-hery et al., 2022), which poses a challenge: text datasets rich in social intelligence and interactions are not easily found naturally due to reporting biases, and they are costly to create (Rashkin et al., 2018; Mostafazadeh et al., 2020).”
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning (2019, background): “…common-sense, such as CommonsenseQA (Talmor et al., 2018) and Social IQa (Sap et al., 2019), and various other types of commonsense inferences (Levesque et al., 2012; Rahman and Ng, 2012; Gordon, 2016; Rashkin et al., 2018; Roemmele et al., 2011; Mostafazadeh et al., 2017; Zellers et al., 2018).”
Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches (2019, methodology): “They may be less restricted to a subset of a large number of category labels, e.g., the Maslow, Reiss, and Plutchik tasks in Story Commonsense (Rashkin et al., 2018a).”
Machine Common Sense Concept Paper (2018, background): “Yejin Choi, a new faculty member at UW, has led a series of projects to learn commonsense knowledge from language on the Web (e.g., verb physics [39], event inferences [40], story understanding [41]).”
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning (2020, methodology): “It was used for the story cloze task whose goal was to predict the story ending from its ﬁrst 4 sentences, but gained popularity and became the base of additional benchmarks (Rashkin et al., 2018).”
DREAM: Uncovering Mental Models behind Language Models (2021, methodology): “We use three existing commonsense resources to construct a training dataset for learning mental models: 1) Story commonsense (Rashkin et al., 2018) 2) Social Chemistry (Forbes et al., 2020) 3) Moral stories (Emelin et al., 2020) Statistics about these data sources, and which di-mension(s) they…”
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (2019, background): “Event2Mind (Rashkin et al., 2018b) models stereotypical intents and reactions of people, described in short free-form text.”
GLUCOSE: GeneraLized and COntextualized Story Explanations (2020, background): “Recently, there has been a renewed interest in commonsense reasoning (Talmor et al., 2019; Tandon et al., 2019; Rashkin et al., 2018a; Zellers et al., 2018), further fostered by the increasing need for explainable AI systems (Yang et al., 2018).”
Natural Language Processing with Commonsense Knowledge: A Survey (2021, background): “Notable benchmarks are: • Story Commonsense [75] is a large-scale dataset containing detailed annotations that link commonsense story events to the mental states of characters, even when these states are not explicitly mentioned.”
A Chapter-Wise Understanding System for Text-To-Speech in Chinese Novels (2021, background): “Some researches has been conducted to detect emotions from texts in such ﬁne-grained levels in social media and commonsense stories in English [13, 14] and Chinese [15].”
I Know the Feeling: Learning to Converse with Empathy (2018, background): “Other possible directions would be to see if this data can serve as additional weakly supervised data for more complex emotion-related tasks that look at emotion evolution or causality (Gui et al., 2016; Rashkin et al., 2018).”
Automated Storytelling via Causal, Commonsense Plot Ordering (2020, background): “Rashkin et al. (2018) present an annotation framework speciﬁcally designed to examine the mental states of characters in commonsense based stories.”