DSpace@RPI is a repository of Rensselaer Polytechnic Institute's theses and dissertations which are available in digital format, largely from 2006 to present, along with other selected resources.

Recent Submissions

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    Towards a Progression-Aware Autonomous Dialogue Agent
    (Association for Computational Linguistics, 2022-07-10) Sanders, Abraham; Strzalkowski, Tomek; Si, Mei; Chang, Albert; Dey, Deepanshu; Braasch, Jonas; Wang, Dakuo
    Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios spanning a diverse set of tasks, from general chit-chat to focused goal-oriented discourse. While these agents excel at generating high-quality responses that are relevant to prior context, they suffer from a lack of awareness of the overall direction in which the conversation is headed, and the likelihood of task success inherent therein. Thus, we propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes, and use this signal to inform planning for subsequent responses. Our framework is composed of three key elements: (1) the notion of a “global” dialogue state (GDS) space, (2) a task-specific progression function (PF) computed in terms of a conversation’s trajectory through this space, and (3) a planning mechanism based on dialogue rollouts by which an agent may use progression signals to select its next response.
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    Adapting Emotion Detection to Analyze Influence Campaigns on Social Media
    (Association for Computational Linguistics, 2023-07-01) Bhaumik, Ankita; Bernhardt, Andy; Katsios, Gregorios A; Sa, Ning; Strzalkowski, Tomek
    Social media is an extremely potent tool for influencing public opinion, particularly during important events such as elections, pandemics, and national conflicts. Emotions are a crucial aspect of this influence, but detecting them accurately in the political domain is a significant challenge due to the lack of suitable emotion labels and training datasets. In this paper, we present a generalized approach to emotion detection that can be adapted to the political domain with minimal performance sacrifice. Our approach is designed to be easily integrated into existing models without the need for additional training or fine-tuning. We demonstrate the zero-shot and few-shot performance of our model on the 2017 French presidential elections and propose efficient emotion groupings that would aid in effectively analyzing influence campaigns and agendas on social media.
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    Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media
    (International Committee on Computational Linguistics (ICCL), 2024-05-20) Katsios, Gregorios A; Sa, Ning; Bhaumik, Ankita; Strzalkowski, Tomek
    The behavior and decision making of groups or communities can be dramatically influenced by individuals pushing particular agendas, e.g., to promote or disparage a person or an activity, to call for action, etc.. In the examination of online influence campaigns, particularly those related to important political and social events, scholars often concentrate on identifying the sources responsible for setting and controlling the agenda (e.g., public media). In this article we present a methodology for detecting specific instances of agenda control through social media where annotated data is limited or non-existent. By using a modest corpus of Twitter messages centered on the 2022 French Presidential Elections, we carry out a comprehensive evaluation of various approaches and techniques that can be applied to this problem. Our findings demonstrate that by treating the task as a textual entailment problem, it is possible to overcome the requirement for a large annotated training dataset.
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    Social Convos: Capturing Agendas and Emotions on Social Media
    (International Committee on Computational Linguistics (ICCL), 2024-05-20) Bhaumik, Ankita; Sa, Ning; Katsios, Gregorios A; Strzalkowski, Tomek
    Social media platforms are popular tools for disseminating targeted information during major public events like elections or pandemics. Systematic analysis of the message traffic can provide valuable insights into prevailing opinions and social dynamics among different segments of the population. We are specifically interested in influence spread, and in particular whether more deliberate influence operations can be detected. However, filtering out the essential messages with telltale influence indicators from the extensive and often chaotic social media traffic is a major challenge. In this paper we present a novel approach to extract influence indicators from messages circulating among groups of users discussing particular topics. We build upon the concept of a convo to identify influential authors who are actively promoting some particular agenda around that topic within the group. We focus on two influence indicators: the (control of) agenda and the use of emotional language.
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    Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling
    (Association for Computational Linguistics, 2024-06-12) Katsios, Gregorios A; Sa, Ning; Strzalkowski, Tomek
    The identification of Figurative Language (FL) features in text is crucial for various Natural Language Processing (NLP) tasks, where understanding of the author's intended meaning and its nuances is key for successful communication. At the same time, the use of a specific blend of various FL forms most accurately reflects a writer's style, rather than the use of any single construct, such as just metaphors or irony. Thus, we postulate that FL features could play an important role in Authorship Attribution (AA) tasks. We believe that our is the first computational study of AA based on FL use. Accordingly, we propose a Multi-task Figurative Language Model (MFLM) that learns to detect multiple FL features in text at once. We demonstrate, through detailed evaluation across multiple test sets, that the our model tends to perform equally or outperform specialized binary models in FL detection. Subsequently, we evaluate the predictive capability of joint FL features towards the AA task on three datasets, observing improved AA performance through the integration of MFLM embeddings.

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