Papers by Shivani Kumar
Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues (2024.findings-eacl)
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| Challenge: | blending multiple languages within a single conversation presents a formidable challenge, given the wide-ranging variations influenced by individual speaking styles and cultural backgrounds. |
| Approach: | They propose a novel approach to harness the Big Five personality traits acquired in an unsupervised manner from code-mixed conversations to bolster the performance of response generation. |
| Outcome: | The proposed approach enhances contextual relevance and performance of the proposed model by combining personality traits with dialogue context. |
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues (2022.acl-long)
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| Challenge: | Indirect speech achieves a constellation of discourse goals in human communication, but it is challenging for AI agents to comprehend such idiosyncrasies. |
| Approach: | They propose a task to generate natural language explanations of satirical conversations using a multimodal and code-mixed dataset to capture multimodality. |
| Outcome: | The proposed task generates natural language explanations of satirical conversations in a multimodal and code-mixed setting and surpasses baselines on almost all metrics. |
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues (2023.emnlp-main)
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| Challenge: | Understanding emotions during conversation is a fundamental aspect of human communication. |
| Approach: | They propose an approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions. |
| Outcome: | The proposed approach improves ERC for code-mixed conversations by integrating commonsense with dialogue context. |
Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning as a Computational Pipeline with UniMoral (2025.acl-long)
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| Challenge: | Existing approaches to analyze moral reasoning are discordant and lack cohesion, focusing on isolated aspects of the process. |
| Approach: | They propose a unified dataset that integrates moral dilemmas annotated with labels for action choices, ethical principles, contributing factors, and consequences, and captures diverse socio-cultural contexts. |
| Outcome: | The proposed dataset integrates moral dilemmas annotated with labels for action choices, ethical principles, contributing factors, and consequences, along with annotators’ moral and cultural profiles. |
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)
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Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens
| Challenge: | Recent work has sought to use large language models to simulate human-human and human-LLM interactions. |
| Approach: | They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts. |
| Outcome: | The proposed models perform similarly in simulating English, Chinese, and Russian dialogues. |
Adding SPICE to Life: Speaker Profiling in Multiparty Conversations (2024.lrec-main)
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| Challenge: | Prior studies assumed the speaker’s persona’s immediate availability, a premise not universally applicable. |
| Approach: | They propose to synthesize persona attributes for each dialogue participant by combining three core tasks: persona discovery, persona-type identification, and persona value extraction. |
| Outcome: | The proposed task synthesizes persona attributes for each dialogue participant . the resulting model is compared against a baseline model and the proposed model is robust. |