Papers by Zeynab Raeesy
Learning to Retrieve Engaging Follow-Up Queries (2023.findings-eacl)
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Christopher Richardson, Sudipta Kar, Anjishnu Kumar, Anand Ramachandran, Zeynab Raeesy, Omar Khan, Abhinav Sethy
| Challenge: | Open domain conversational agents can answer a wide range of targeted queries, but knowledge exploration is a lengthy task. |
| Approach: | They propose a retrieval based system for predicting the next questions that the user might have . they train ranking models on a dataset called the Follow-up Query Bank . |
| Outcome: | The proposed system can proactively assist users in knowledge exploration leading to a more engaging dialog. |
Generating Contextual Images for Long-Form Text (2024.lrec-main)
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| Challenge: | Recent advances in Text-to-Image models require short prompts that describe both the content and style of the target image. |
| Approach: | They propose to use Large Language Models (LLMs) and Text-to-Image Models to synthesize relevant visual imagery from generic long-form text. |
| Outcome: | The proposed models can generate high-quality images from short prompts that describe both the content and style of the target image. |
Unified Contextual Query Rewriting (2023.acl-industry)
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Yingxue Zhou, Jie Hao, Mukund Rungta, Yang Liu, Eunah Cho, Xing Fan, Yanbin Lu, Vishal Vasudevan, Kellen Gillespie, Zeynab Raeesy
| Challenge: | Large-scale conversational AI agents such as Alexa, Siri, and Google Assistant are becoming increasingly popular in real-world applications to assist users in daily life. |
| Approach: | They propose a unified contextual query rewriting model that unifies QR for friction reduction and contextual carryover . they leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance . |
| Outcome: | The proposed model reduces friction and contextual carryover by using multiple auxiliary tasks. |
Multimodal Context Carryover (2022.emnlp-industry)
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Prashan Wanigasekara, Nalin Gupta, Fan Yang, Emre Barut, Zeynab Raeesy, Kechen Qin, Stephen Rawls, Xinyue Liu, Chengwei Su, Spurthi Sandiri
| Challenge: | Existing voice-only dialogue systems lack multimodality support, which can lead to costly system redesigns. |
| Approach: | They propose to augment existing voice-only dialogue systems with additional multimodal components to facilitate quick delivery of visual modality support with minimal changes. |
| Outcome: | The proposed framework improves visual modality support with minimal changes on an in-house multi-modal visual navigation data set. |