Papers by Julia Kiseleva
Learning to Decompose and Organize Complex Tasks (2021.naacl-main)
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| Challenge: | Using a novel end-to-end pipeline, we propose a solution that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Approach: | They propose a pipeline that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Outcome: | The proposed pipeline outperforms state-of-the-art graph induction pipelines in a dataset of complex tasks with their sub-task graphs. |
Assessing and Verifying Task Utility in LLM-Powered Applications (2024.emnlp-main)
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Negar Arabzadeh, Siqing Huo, Nikhil Mehta, Qingyun Wu, Chi Wang, Ahmed Awadallah, Charles Clarke, Julia Kiseleva
| Challenge: | Rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks. |
| Approach: | They propose a framework to propose criteria tailored to the unique purpose of any given application and propose corresponding criteria for the application. |
| Outcome: | The proposed framework provides a comprehensive assessment of the effectiveness and robustness of two open source datasets including Math Problem solving and ALFWorld House-hold related tasks. |
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions (2021.emnlp-main)
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| Challenge: | Recent advances on neural approaches to natural language processing have triggered a renaissance in end-to-end neural open-domain chatbots. |
| Approach: | They propose to use offline and online steps to evaluate the quality of clarifying questions in various open-domain dialogues to improve the quality and accuracy of the system response. |
| Outcome: | The proposed pipeline is suitable as a foundation for further research. |
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems (2020.findings-emnlp)
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| Challenge: | Dialogue policy learning for task-oriented dialogue systems has enjoyed great progress through using reinforcement learning methods. |
| Approach: | They propose a dialogue action decoder and a simulator-free adversarial learning method to improve dialogue agent performance without using reinforcement learning. |
| Outcome: | The proposed methods achieve more stable and higher performance with fewer efforts, such as the domain knowledge required to design a user simulator and the intractable parameter tuning in reinforcement learning. |
PREME: Preference-based Meeting Exploration through an Interactive Questionnaire (2023.findings-eacl)
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Negar Arabzadeh, Ali Ahmadvand, Julia Kiseleva, Yang Liu, Ahmed Hassan Awadallah, Ming Zhong, Milad Shokouhi
| Challenge: | Recent studies show that providing meeting summaries does not align with current approaches to document summarization. |
| Approach: | They propose a framework for generating questionnaires for preference-based meeting exploration . they measure how much questions are answerable to ensure factual correctness . |
| Outcome: | The proposed framework provides a list of suggested questions reflecting user preferences . it measures how much questions are answerable to ensure factual correctness . |
What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research (2022.naacl-main)
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| Challenge: | Existing research on automatic text summarization does not fully align with students’ needs. |
| Approach: | They propose a survey methodology that can be used to investigate the needs of users of automatically generated summaries. |
| Outcome: | The proposed method can be easily adjusted to investigate different user groups. |
Guided Dialogue Policy Learning without Adversarial Learning in the Loop (2020.findings-emnlp)
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Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao
| Challenge: | Reinforcement learning methods suffer from sparse and unstable reward signals . alternating training of dialogue agent and reward model can get stuck in local optima . |
| Approach: | They propose to decompose adversarial training into two steps to improve dialogue policy learning. |
| Outcome: | The proposed method achieves remarkable task success rate using both on-policy and off-poly reinforcement learning methods. |
Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback (2024.findings-eacl)
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| Challenge: | In many approaches to Natural Language Processing tasks, language is inherently interactive. |
| Approach: | They propose to use human-AI collaboration to improve human-human interaction by providing feedback that the agent can understand and utilize. |
| Outcome: | The proposed task is an interactive grounded language understanding task in a MineCraft-like world. |