Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types

Saldias, Belen; Roy, Deb

doi:10.18653/v1/2020.nuse-1.10

Cited by 7 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other work has explored ways to bridge the features of a story and human-perceived similarity of stories (Nguyen et al, 2014). Saldias and Roy (2020) found that people use Labov's action (series of events) and evaluation (narrator's needs and desires) clauses to identify similarity in personal narratives (Labov and Waletzky, 1997). Their findings support our decision to focus on modeling events, emotions, and morals within stories.…”

Section: Related Workmentioning

confidence: 60%

See 1 more Smart Citation

Modeling Empathic Similarity in Personal Narratives

Shen,

Sap,

Colon-Hernandez

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

The most meaningful connections between people are often fostered through expression of shared vulnerability and emotional experiences in personal narratives. We introduce a new task of identifying similarity in personal stories based on empathic resonance, i.e., the extent to which two people empathize with each others' experiences, as opposed to raw semantic or lexical similarity, as has predominantly been studied in NLP. Using insights from social psychology, we craft a framework that operationalizes empathic similarity in terms of three key features of stories: main events, emotional trajectories, and overall morals or takeaways. We create EM-PATHICSTORIES, a dataset of 1,500 personal stories annotated with our empathic similarity features, and 2,000 pairs of stories annotated with empathic similarity scores. Using our dataset, we finetune a model to compute empathic similarity of story pairs, and show that this outperforms semantic similarity models on automated correlation and retrieval metrics. Through a user study with 150 participants, we also assess the effect our model has on retrieving stories that users empathize with, compared to naive semantic similarity-based retrieval, and find that participants empathized significantly more with stories retrieved by our model. Our work has strong implications for the use of empathy-aware models to foster human connection and empathy between people.

show abstract

Section: Related Workmentioning

confidence: 60%

“…(3) Spoken Personal Narratives. We use stories from the Roadtrip Nation corpus (Saldias and Roy, 2020), which contains transcribed personal stories about people's career trajectories and life stories.…”

Section: Data Sourcesmentioning

confidence: 99%

Modeling Empathic Similarity in Personal Narratives

Shen,

Sap,

Colon-Hernandez

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

show abstract

“…Swanson et al [69] developed a method for detecting narrative clause types and proposed a labeling algorithm for analyzing personal stories. Saldias and Roy [65] created a Natural Language Processing (NLP) model for understanding similar aspects of personal narratives. Fan and Presner [18] conducted an algorithmic close reading of Holocaust testimonies using a computational system to detect agency, evaluation, and orientation from interview transcripts.…”

Section: Analyzing the Semantic Relations In Datamentioning

confidence: 99%

Mining Semantic Relations in Data References to Understand the Roles of Research Data in Academic Literature

Fan,

Lafia,

Wofford

et al. 2023

2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

View full text Add to dashboard Cite

Research data serves important roles in scientific discovery and academic innovation. To appropriately assign credit for data work and to measure the value of research data, it is essential to articulate how data are actually used in research. We leveraged a combination of computational methods and human analysis to characterize different types of data use by mining semantic relations from the phrases where data are referenced in academic literature. In particular, we investigated references to data in the bibliography of a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). After retrieving and extracting semantic relations as subject-relation-object triples, we used rule-based methods to classify them. We then annotated samples from 11 frequent classes of data reference triples and found that they vary primarily along two dimensions of data use: proximity and function. Proximity describes the distance between the author and the data they reference (e.g., direct or indirect engagement). Function describes the role that data plays in each reference (e.g., describing interaction or providing context). These semantic relationships between authors and data reveal the ways data are used in scientific publications. Evidence of the variety of ways data are used can help stakeholders in research data curation and stewardshipincluding data providers, data curators, and data users -recognize the myriad ways that their investments in data sharing are realized.

show abstract

“…Others rely on annotations for classifying complex aspects of personal narratives, such as identifying the intention of the narrator [11,14] or where there is subjectivity [2,15]. We have found others who use pre-existing narrative models from the psychology community [16].…”

Section: Related Workmentioning

confidence: 99%

Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community

Giorgi¹,

Zhao²,

Feng³

et al. 2023

Preprint

View full text Add to dashboard Cite

In the r/AmITheAsshole subreddit, people anonymously share first person narratives that contain some moral dilemma or conflict and ask the community to judge who is at fault (i.e., who is "the asshole"). In general, first person narratives are a unique storytelling domain where the author is the narrator (the person telling the story) but can also be a character (the person living the story) and, thus, the author has two distinct voices presented in the story. In this study, we identify linguistic and narrative features associated with the author as the character or as a narrator. We use these features to answer the following questions: (1) what makes an asshole character and ( 2) what makes an asshole narrator? We extract both Author-as-Character features (e.g., demographics, narrative event chain, and emotional arc) and Author-as-Narrator features (i.e., the style and emotion of the story as a whole) in order to identify which aspects of the narrative are correlated with the final moral judgment. Our work shows that "assholes" as Characters frame themselves as lacking agency with a more positive personal arc, while "assholes" as Narrators will tell emotional and opinionated stories.1 "OP" and "author" will be used interchangeably throughout the paper. This is not to be confused with "narrator," which we are considering to be the style of the written work.

show abstract

Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types

Cited by 7 publications

References 23 publications

Modeling Empathic Similarity in Personal Narratives

Modeling Empathic Similarity in Personal Narratives

Mining Semantic Relations in Data References to Understand the Roles of Research Data in Academic Literature

Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community

Contact Info

Product

Resources

About