Deb Roy

Also published as: Suman Deb Roy


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Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis
Hang Jiang | Yining Hua | Doug Beeferman | Deb Roy
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Social media data such as Twitter messages (“tweets”) pose a particular challenge to NLP systems because of their short, noisy, and colloquial nature. Tasks such as Named Entity Recognition (NER) and syntactic parsing require highly domain-matched training data for good performance. To date, there is no complete training corpus for both NER and syntactic analysis (e.g., part of speech tagging, dependency parsing) of tweets. While there are some publicly available annotated NLP datasets of tweets, they are only designed for individual tasks. In this study, we aim to create Tweebank-NER, an English NER corpus based on Tweebank V2 (TB2), train state-of-the-art (SOTA) Tweet NLP models on TB2, and release an NLP pipeline called Twitter-Stanza. We annotate named entities in TB2 using Amazon Mechanical Turk and measure the quality of our annotations. We train the Stanza pipeline on TB2 and compare with alternative NLP frameworks (e.g., FLAIR, spaCy) and transformer-based models. The Stanza tokenizer and lemmatizer achieve SOTA performance on TB2, while the Stanza NER tagger, part-of-speech (POS) tagger, and dependency parser achieve competitive performance against non-transformer models. The transformer-based models establish a strong baseline in Tweebank-NER and achieve the new SOTA performance in POS tagging and dependency parsing on TB2. We release the dataset and make both the Stanza pipeline and BERTweet-based models available “off-the-shelf” for use in future Tweet NLP research. Our source code, data, and pre-trained models are available at:

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CommunityLM: Probing Partisan Worldviews from Language Models
Hang Jiang | Doug Beeferman | Brandon Roy | Deb Roy
Proceedings of the 29th International Conference on Computational Linguistics

As political attitudes have diverged ideologically in the United States, political speech has diverged lingusitically. The ever-widening polarization between the US political parties is accelerated by an erosion of mutual understanding between them. We aim to make these communities more comprehensible to each other with a framework that probes community-specific responses to the same survey questions using community language models CommunityLM. In our framework we identify committed partisan members for each community on Twitter and fine-tune LMs on the tweets authored by them. We then assess the worldviews of the two groups using prompt-based probing of their corresponding LMs, with prompts that elicit opinions about public figures and groups surveyed by the American National Election Studies (ANES) 2020 Exploratory Testing Survey. We compare the responses generated by the LMs to the ANES survey results, and find a level of alignment that greatly exceeds several baseline methods. Our work aims to show that we can use community LMs to query the worldview of any group of people given a sufficiently large sample of their social media discussions or media diet.


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Lifelong Knowledge-Enriched Social Event Representation Learning
Prashanth Vijayaraghavan | Deb Roy
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

The ability of humans to symbolically represent social events and situations is crucial for various interactions in everyday life. Several studies in cognitive psychology have established the role of mental state attributions in effectively representing variable aspects of these social events. In the past, NLP research on learning event representations often focuses on construing syntactic and semantic information from language. However, they fail to consider the importance of pragmatic aspects and the need to consistently update new social situational information without forgetting the accumulated experiences. In this work, we propose a representation learning framework to directly address these shortcomings by integrating social commonsense knowledge with recent advancements in the space of lifelong language learning. First, we investigate methods to incorporate pragmatic aspects into our social event embeddings by leveraging social commonsense knowledge. Next, we introduce continual learning strategies that allow for incremental consolidation of new knowledge while retaining and promoting efficient usage of prior knowledge. Experimental results on event similarity, reasoning, and paraphrase detection tasks prove the efficacy of our social event embeddings.


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DAPPER: Learning Domain-Adapted Persona Representation Using Pretrained BERT and External Memory
Prashanth Vijayaraghavan | Eric Chu | Deb Roy
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Research in building intelligent agents have emphasized the need for understanding characteristic behavior of people. In order to reflect human-like behavior, agents require the capability to comprehend the context, infer individualized persona patterns and incrementally learn from experience. In this paper, we present a model called DAPPER that can learn to embed persona from natural language and alleviate task or domain-specific data sparsity issues related to personas. To this end, we implement a text encoding strategy that leverages a pretrained language model and an external memory to produce domain-adapted persona representations. Further, we evaluate the transferability of these embeddings by simulating low-resource scenarios. Our comparative study demonstrates the capability of our method over other approaches towards learning rich transferable persona embeddings. Empirical evidence suggests that the learnt persona embeddings can be effective in downstream tasks like hate speech detection.

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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.


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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.


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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.


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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)


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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


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


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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)


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


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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


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


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

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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

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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