An official website of the United States government
Community organizers build grassroots power and collective voice in communities that are structurally marginalized in representative democracy, particularly in minoritized communities. Our project explores how self-identified community organizers use the narrative potentials of data to navigate the promises of data activism and the simultaneous risks posed to working-class communities of color by data-intensive technologies. Our nine respondents consistently named the material, financial, intellectual, and affective demands of data work, as well as the provisional, tenuous possibility of accomplishing movement work via narratives bolstered by data. Our early results identified two important factors in community organizers’ assessment of the efficacy and political potential of narratives built with data: audience and legitimacy.
more »
« less
Affective and Dynamic Beam Search for Story Generation
Storytelling’s captivating potential makes it a fascinating research area, with implications for entertainment, education, therapy, and cognitive studies. In this paper, we propose Affective Story Generator (AffGen) for generating interesting narratives. AffGen introduces ‘intriguing twists’ in narratives by employing two novel techniques—Dynamic Beam Sizing and Affective Reranking. Dynamic Beam Sizing encourages less predictable, more captivating word choices using a contextual multi-arm bandit model. Affective Reranking prioritizes sentence candidates based on affect intensity. Our empirical evaluations, both automatic and human, demonstrate AffGen’s superior performance over existing baselines in generating affectively charged and interesting narratives. Our ablation study and analysis provide insights into the strengths and weaknesses of AffGen.
more »
« less
- Award ID(s):
- 2105329
- PAR ID:
- 10482432
- Publisher / Repository:
- Association for Computational Linguistics
- Date Published:
- Journal Name:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Page Range / eLocation ID:
- 11792 to 11806
- Format(s):
- Medium: X
- Location:
- Singapore
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Research suggests autobiographical memory plays an important role in shaping identity and directing future behavior. This mixed-methods exploratory study examined the narrative qualities of autobiographical memories related to students’ interest in careers in STEM (Science, Technology, Engineering, and Math) fields and their associations with scores on measures of science identity, imposter phenomenon, and academic achievement (i.e., grades in STEM courses and number of STEM courses taken) among 105 participants of a U.S. National Science Foundation-funded academic support program for low-income STEM students. The inductive categorization of participants’ narratives revealed ten distinct thematic domains: family and peer influences, transformative STEM experiences, career shifts, personal growth, overcoming adversity, role models, early interest, supportive mentorship, diversity and representation, and recognition of aptitude. These narratives were further examined for vocational needs and values based on the Theory of Work Adjustment. Intrinsic motivation, achievement, and status were the most prevalent vocational values identified, followed by altruism, autonomy, comfort, and safety. Finally, we applied the Narrative Identity Framework to assess each narrative in terms of motivational and affective themes (i.e., agency, communion, affective tone), autobiographical reasoning (i.e., meaning-making), and structural aspects (i.e., specificity, context, chronology). Higher narrative specificity was prospectively associated with a greater number of STEM courses completed. Science identity was positively associated with narrative context, theme, and meaning-making. The current study underscores the potential relevance of personal storytelling to academic adjustment and lays the groundwork for future research to elucidate the mechanisms linking personal narratives to STEM engagement, persistence, and achievement.more » « less
-
We study generating abstractive summaries that are faithful and factually consistent with the given articles. A novel contrastive learning formulation is presented, which leverages both reference summaries, as positive training data, and automatically generated erroneous summaries, as negative training data, to train summarization systems that are better at distinguishing between them. We further design four types of strategies for creating negative samples, to resemble errors made commonly by two state-of-the-art models, BART and PEGASUS, found in our new human annotations of summary errors. Experiments on XSum and CNN/Daily Mail show that our contrastive learning framework is robust across datasets and models. It consistently produces more factual summaries than strong comparisons with post error correction, entailmentbased reranking, and unlikelihood training, according to QA-based factuality evaluation. Human judges echo the observation and find that our model summaries correct more errors.more » « less
-
Abstract BackgroundReal‐world engineering problems are ill‐defined and complex, and solving them may arouse negative epistemic affect (feelings experienced within problem‐solving). These feelings fall into sequenced patterns (affective pathways). Over time, these patterns can alter students' attitudes toward engineering. Meta‐affect (affect or cognition about affect) can shape or reframe affective pathways, changing a student's problem‐solving experience. Purpose/Hypothesis(es)This paper examines epistemic affect and meta‐affect in undergraduate students solving ill‐defined problems called open‐ended modeling problems (OEMPs), addressing two research questions: What epistemic affect and transitions between different affective states do students report? And, how does meta‐affect shape students' affective experiences? Design/MethodWe examined 11 retrospective interviews with nine students performed across two semesters in which students completed OEMPs. Using inductive and deductive coding with discourse analysis, we systematically searched for expressions conveying epistemic affect and for transitions in affect; we performed additional deductive coding of the transcripts for meta‐affect and synthesized these results to formulate narratives related to affect and meta‐affect. ResultsTogether, the expressions, transitions, and meta‐affect suggest different types of student experiences. Depending on their meta‐affect, students either recounted experiences dominated by positive or negative affect, or else they experienced negative emotions as productive. ConclusionsIll‐defined complex problems elicit a wide range of positive and negative emotions and provide opportunities to practice affective regulation and productive meta‐affect. Viewing the OEMPs as authentic disciplinary experiences and/or the ability to view negative emotions as productive can enable overall positive experiences. Our results provide insight into how instructors can foster positive affective pathways through problem‐scaffolding or their interactions with students.more » « less
-
Narrative planning is the process of generating sequences of actions that form coherent and goal-oriented narratives. Classical implementations of narrative planning rely on heuristic search techniques to offer structured story generation but face challenges with scalability due to large branching factors and deep search requirements. Large Language Models (LLMs), with their extensive training on diverse linguistic datasets, excel in understanding and generating coherent narratives. However, their planning ability lacks the precision and structure needed for effective narrative planning. This paper explores a hybrid approach that uses LLMs as heuristic guides within classical search frameworks for narrative planning. We compare various prompt designs to generate LLM heuristic predictions and evaluate their performance against h+, hmax, and relaxed plan heuristics. Additionally, we analyze the ability of relaxed plans to predict the next action correctly, comparing it to the LLMs’ ability to make the same prediction. Our findings indicate that LLMs rarely exceed the accuracy of classical planning heuristics.more » « less
