arXiv subjects

Aryaan Shaikh

Publications and source records attributed to Aryaan Shaikh.

2 recordsLinked to original sources

arXiv2026

E3VA: Enhancing Emotional Expressiveness in Virtual Conversational Agents

With the advent of generative AI and large language models, embodied conversational agents are becoming synonymous with online interactions. These agents possess vast amounts of knowledge but suffer from exhibiting limited emotional expressiveness. Without adequate expressions, agents might fail to adapt to users' emotions, which may result in a sub-optimal user experience and engagement. Most current systems prioritize content based responses, neglecting the emotional context of conversations. Research in this space is currently limited to specific contexts, like mental health. To bridge this gap, our project proposes the implementation of expressive features in a virtual conversational agent which will utilize sentiment analysis and natural language processing to inform the generation of empathetic, expressive responses. The project delivers a functional conversational agent capable of assessing and responding to user emotions accordingly. We posit this will enhance usability, engagement, and the overall quality of conversations and present results from an exploratory pilot study investigating the same.

cs.HC↗

arXiv2025

DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization

Query-focused tabular summarization is an emerging task in table-to-text generation that synthesizes a summary response from tabular data based on user queries. Traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. To address these challenges, we introduce DETQUS (Decomposition-Enhanced Transformers for QUery-focused Summarization), a system designed to improve summarization accuracy by leveraging tabular decomposition alongside a fine-tuned encoder-decoder model. DETQUS employs a large language model to selectively reduce table size, retaining only query-relevant columns while preserving essential information. This strategy enables more efficient processing of large tables and enhances summary quality. Our approach, equipped with table-based QA model Omnitab, achieves a ROUGE-L score of 0.4437, outperforming the previous state-of-the-art REFACTOR model (ROUGE-L: 0.422). These results highlight DETQUS as a scalable and effective solution for query-focused tabular summarization, offering a structured alternative to more complex architectures.

cs.CL↗