Rohan Das
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Publications
ACL
A Structured Clustering Approach for Inducing Media Narratives
Rohan Das
, Advait Deshmukh, Alexandria Leto, Zohar Naaman, I-Ta Lee, Maria Leonor Pacheco
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, 2026
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ACL
arXiv
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BibTeX
BibTeX
@inproceedings{das-etal-2026-structured, title = "A Structured Clustering Approach for Inducing Media Narratives", author = "Das, Rohan and Deshmukh, Advait and Leto, Alexandria and Naaman, Zohar and Lee, I-Ta and Pacheco, Maria Leonor", editor = "Liakata, Maria and Moreira, Viviane P. and Zhang, Jiajun and Jurgens, David", booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)", month = jul, year = "2026", address = "San Diego, California, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2026.acl-long.1970/", pages = "42544--42577", ISBN = "979-8-89176-390-6", abstract = "Media narratives wield tremendous power in shaping public opinion, yet computational approaches struggle to capture the nuanced storytelling structures that communication theory emphasizes as central to how meaning is constructed. Existing approaches either miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. To bridge this gap, we present a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. Our approach produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation." }
ACL
Effects of Collaboration on the Performance of Interactive Theme Discovery Systems
Alvin Po-Chun Chen*,
Rohan Das
*, Dananjay Srinivas*, Alexandra Barry, Maksim Seniw, Maria Leonor Pacheco
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, 2026
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ACL
arXiv
Code
BibTeX
BibTeX
@inproceedings{chen-etal-2026-effects, title = "Effects of Collaboration on the Performance of Interactive Theme Discovery Systems", author = "Chen, Alvin Po-Chun and Das, Rohan and Srinivas, Dananjay and Barry, Alexandra and Seniw, Maksim and Pacheco, Maria Leonor", editor = "Liakata, Maria and Moreira, Viviane P. and Zhang, Jiajun and Jurgens, David", booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)", month = jul, year = "2026", address = "San Diego, California, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2026.acl-long.1968/", pages = "42507--42526", ISBN = "979-8-89176-390-6", abstract = "NLP-assisted solutions to support qualitative data analysis have gained considerable traction. However, no unified evaluation framework exists which can account for the many different settings in which qualitative researchers may employ them. In this paper, we propose a framework to evaluate the way collaboration settings may produce different research outcomes across a variety of interactive systems. Specifically, we study the impact of synchronous vs. asynchronous collaboration using three different NLP-assisted qualitative research tools and present a comprehensive analysis of the differences in the consistency, cohesiveness, and correctness of their outcomes." }
WNU
Media Framing through the Lens of Event-Centric Narratives
Rohan Das
, Aditya Chandra, I-Ta Lee, Maria Leonor Pacheco
Proceedings of the 6th Workshop on Narrative Understanding at EMNLP 2024
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ACL
arXiv
BibTeX
BibTeX
@inproceedings{das-etal-2024-media, title = "Media Framing through the Lens of Event-Centric Narratives", author = "Das, Rohan and Chandra, Aditya and Lee, I-Ta and Pacheco, Maria Leonor", editor = "Lal, Yash Kumar and Clark, Elizabeth and Iyyer, Mohit and Chaturvedi, Snigdha and Brei, Anneliese and Brahman, Faeze and Chandu, Khyathi Raghavi", booktitle = "Proceedings of the The 6th Workshop on Narrative Understanding", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.wnu-1.15", pages = "85--98", abstract = "From a communications perspective, a frame defines the packaging of the language used in such a way as to encourage certain interpretations and to discourage others. For example, a news article can frame immigration as either a boost or a drain on the economy, and thus communicate very different interpretations of the same phenomenon. In this work, we argue that to explain framing devices we have to look at the way narratives are constructed. As a first step in this direction, we propose a framework that extracts events and their relations to other events, and groups them into high-level narratives that help explain frames in news articles. We show that our framework can be used to analyze framing in U.S. news for two different domains: immigration and gun control.", }
NLLP
On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software
Dananjay Srinivas*,
Rohan Das
*, Saeid Tizpaz-Niari, Ashutosh Trivedi, Maria Leonor Pacheco
Proceedings of the 5th Natural Legal Language Processing Workshop at EMNLP 2023
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ACL
arXiv
BibTeX
BibTeX
@inproceedings{srinivas-etal-2023-potential, title = "On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software", author = "Srinivas, Dananjay and Das, Rohan and Tizpaz-Niari, Saeid and Trivedi, Ashutosh and Pacheco, Maria Leonor", editor = "Preo{\textcommabelow{t}}iuc-Pietro, Daniel and Goanta, Catalina and Chalkidis, Ilias and Barrett, Leslie and Spanakis, Gerasimos and Aletras, Nikolaos", booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2023", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.nllp-1.23", doi = "10.18653/v1/2023.nllp-1.23", pages = "230--243", abstract = "Due to the ever-increasing complexity of income tax laws in the United States, the number of US taxpayers filing their taxes using tax preparation software henceforth, tax software) continues to increase. According to the U.S. Internal Revenue Service (IRS), in FY22, nearly 50{\%} of taxpayers filed their individual income taxes using tax software. Given the legal consequences of incorrectly filing taxes for the taxpayer, ensuring the correctness of tax software is of paramount importance. Metamorphic testing has emerged as a leading solution to test and debug legal-critical tax software due to the absence of correctness requirements and trustworthy datasets. The key idea behind metamorphic testing is to express the properties of a system in terms of the relationship between one input and its slightly metamorphosed twinned input. Extracting metamorphic properties from IRS tax publications is a tedious and time-consuming process. As a response, this paper formulates the task of generating metamorphic specifications as a translation task between properties extracted from tax documents - expressed in natural language - to a contrastive first-order logic form. We perform a systematic analysis on the potential and limitations of in-context learning with Large Language Models (LLMs) for this task, and outline a research agenda towards automating the generation of metamorphic specifications for tax preparation software.", }
Peer-Reviewed Non-Archival Work
TADA
A Narrative Graph Approach for Analyzing Framing in News Articles
Aditya Chandra,
Rohan Das
, Chih-Hao Fang, I-Ta Lee, Maria Leonor Pacheco
New Directions in Analyzing Text as Data, 2023