Deb Roy

Also published as: Suman Deb Roy


pdf bib
Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types
Belen Saldias | Deb Roy
Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events

Sharing personal narratives is a fundamental aspect of human social behavior as it helps share our life experiences. We can tell stories and rely on our background to understand their context, similarities, and differences. A substantial effort has been made towards developing storytelling machines or inferring characters’ features. However, we don’t usually find models that compare narratives. This task is remarkably challenging for machines since they, as sometimes we do, lack an understanding of what similarity means. To address this challenge, we first introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts. Second, we ask non-narrative experts to annotate those clauses under Labov’s sociolinguistic model of personal narratives (i.e., action, orientation, and evaluation clause types) and train a classifier that reaches 84.7% F-score for the highest-agreed clauses. Finally, we match stories and explore whether people implicitly rely on Labov’s framework to compare narratives. We show that actions followed by the narrator’s evaluation of these are the aspects non-experts consider the most. Our approach is intended to help inform machine learning methods aimed at studying or representing personal narratives.


pdf bib
Learning Personas from Dialogue with Attentive Memory Networks
Eric Chu | Prashanth Vijayaraghavan | Deb Roy
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The ability to infer persona from dialogue can have applications in areas ranging from computational narrative analysis to personalized dialogue generation. We introduce neural models to learn persona embeddings in a supervised character trope classification task. The models encode dialogue snippets from IMDB into representations that can capture the various categories of film characters. The best-performing models use a multi-level attention mechanism over a set of utterances. We also utilize prior knowledge in the form of textual descriptions of the different tropes. We apply the learned embeddings to find similar characters across different movies, and cluster movies according to the distribution of the embeddings. The use of short conversational text as input, and the ability to learn from prior knowledge using memory, suggests these methods could be applied to other domains.


pdf bib
Twitter Demographic Classification Using Deep Multi-modal Multi-task Learning
Prashanth Vijayaraghavan | Soroush Vosoughi | Deb Roy
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Twitter should be an ideal place to get a fresh read on how different issues are playing with the public, one that’s potentially more reflective of democracy in this new media age than traditional polls. Pollsters typically ask people a fixed set of questions, while in social media people use their own voices to speak about whatever is on their minds. However, the demographic distribution of users on Twitter is not representative of the general population. In this paper, we present a demographic classifier for gender, age, political orientation and location on Twitter. We collected and curated a robust Twitter demographic dataset for this task. Our classifier uses a deep multi-modal multi-task learning architecture to reach a state-of-the-art performance, achieving an F1-score of 0.89, 0.82, 0.86, and 0.68 for gender, age, political orientation, and location respectively.


pdf bib
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
Prashanth Vijayaraghavan | Ivan Sysoev | Soroush Vosoughi | Deb Roy
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


pdf bib
Enhanced Twitter Sentiment Classification Using Contextual Information
Soroush Vosoughi | Helen Zhou | Deb Roy
Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis


pdf bib
A Computational Cognitive Model for Semantic Sub-Network Extraction from Natural Language Queries
Suman Deb Roy | Wenjun Zeng
Proceedings of COLING 2012


pdf bib
Extracting aspects of determiner meaning from dialogue in a virtual world environment
Hilke Reckman | Jeff Orkin | Deb Roy
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)


pdf bib
Grounded Language Modeling for Automatic Speech Recognition of Sports Video
Michael Fleischman | Deb Roy
Proceedings of ACL-08: HLT


pdf bib
Situated Models of Meaning for Sports Video Retrieval
Michael Fleischman | Deb Roy
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers


pdf bib
Intentional Context in Situated Natural Language Learning
Michael Fleischman | Deb Roy
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)


pdf bib
Understanding Complex Visually Referring Utterances
Peter Gorniak | Deb Roy
Proceedings of the HLT-NAACL 2003 Workshop on Learning Word Meaning from Non-Linguistic Data

pdf bib
Conversational Robots: Building Blocks for Grounding Word Meaning
Deb Roy | Kai-Yuh Hsiao | Nikolaos Mavridis
Proceedings of the HLT-NAACL 2003 Workshop on Learning Word Meaning from Non-Linguistic Data

pdf bib
Learning Word Meanings and Descriptive Parameter Spaces from Music
Brian Whitman | Deb Roy | Barry Vercoe
Proceedings of the HLT-NAACL 2003 Workshop on Learning Word Meaning from Non-Linguistic Data