Papers by Ayesha Qamar
MultiCAT: Multimodal Communication Annotations for Teams (2025.findings-naacl)
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Adarsh Pyarelal, John M Culnan, Ayesha Qamar, Meghavarshini Krishnaswamy, Yuwei Wang, Cheonkam Jeong, Chen Chen, Md Messal Monem Miah, Shahriar Hormozi, Jonathan Tong, Ruihong Huang
| Challenge: | Recent flagship models from OpenAI and Google are only capable of 1-on-1 interactions with humans, limiting the potential for integration into human-machine teams of the future. |
| Approach: | They propose a dataset that allows team members to make multiple types of predictions on the same dataset. |
| Outcome: | The proposed dataset builds upon data from teams working collaboratively to save victims in a simulated search and rescue mission. |
Auto Review: Second Stage Error Detection for Highly Accurate Information Extraction from Phone Conversations (2025.acl-industry)
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| Challenge: | Automating benefit verification phone calls saves time and improves patient care. |
| Approach: | They propose a second-stage postprocessing pipeline that reduces manual effort while maintaining a high bar for accuracy. |
| Outcome: | The proposed system significantly reduces manual effort while maintaining a high bar for accuracy while reducing noise and jargon. |
EMONA: Event-level Moral Opinions in News Articles (2024.naacl-long)
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Yuanyuan Lei, Md Messal Monem Miah, Ayesha Qamar, Sai Ramana Reddy, Jonathan Tong, Haotian Xu, Ruihong Huang
| Challenge: | Recent work on news articles has focused on social media short texts, but little has explored moral sentiment within news articles. |
| Approach: | They propose to extract event-level moral opinions from news articles using a new dataset . they use annotated event-based moral opinions to analyze news articles . |
| Outcome: | The proposed dataset consists of 400 news articles containing over 10k sentences and 45k events, among which 9,613 events received moral foundation labels. |
Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification (2023.findings-emnlp)
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| Challenge: | Identifying how speakers interact with each other in a conversation is difficult when more than two interlocutors take part in . To overcome this challenge, we propose to explicitly add speaker awareness to each utterance representation. |
| Approach: | They propose to add speaker awareness to each utterance representation to model how each speaker is behaving within the local context of a conversation. |
| Outcome: | The proposed approach is able to model multiparticipant and dyadic conversations on the MRDA and SwDA datasets and shows that it is more efficient than previous approaches. |
Do LLMs Understand Dialogues? A Case Study on Dialogue Acts (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance on many unseen tasks in a zero-shot setting. |
| Approach: | They propose to identify three key pre-tasks essential for accurate DA prediction: Turn Management, Communicative Function Identification, and Dialogue Structure Prediction. |
| Outcome: | The proposed model fails to outperform basic rule-based tasks on three key pre-tasks, and the results suggest that the model is flawed. |