| Challenge: | RL-based dialog systems require interaction with the environment and obtaining real human users to interact with the system is time-consuming and labor-intensive. |
| Approach: | They propose a method to standardize user simulator building to compare dialog system quality using the same set of user simulators. |
| Outcome: | The proposed method can be used by the community to compare dialog system quality using the same set of user simulators fairly. |
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Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard
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Transferable Dialogue Systems and User Simulators (2021.acl-long)
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| Challenge: | a lack of training data is limiting the development of dialogue systems . we develop a framework for creating dialogue data through self-play between agents . |
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What is wrong with you?: Leveraging User Sentiment for Automatic Dialog Evaluation (2022.findings-acl)
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| Challenge: | Existing metrics for dialog evaluation are trained on human annotations, which is cumbersome to collect. |
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Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions (2021.findings-emnlp)
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Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation (D19-1)
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| Challenge: | Current approaches to visual dialog learning involve an end-to-end framework that maps the multi-modal context to a deep vector and in order to decode a natural dialog response. |
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SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation (2025.acl-industry)
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| Challenge: | Recommender systems are a key component of our day-to-day lives, but evaluation remains a challenge due to the gap between offline metrics and online behaviors. |
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Opportunities and Challenges in Neural Dialog Tutoring (2023.eacl-main)
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| Challenge: | Existing approaches to designing dialog tutors have been challenging . current approaches perform poorly in constrained learning scenarios, authors find . |
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Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System (2021.findings-acl)
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| Challenge: | et al., 2013) show that dialog policy learning is an important component of the task-oriented dialogue system. |
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Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (2020.acl-main)
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| Challenge: | Existing automatic dialog evaluation metrics are mostly reference-based . Existing models that measure self-reported user ratings are biased and variance among different users. |
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Evaluating Dialogue Generation Systems via Response Selection (2020.acl-main)
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| Challenge: | Existing automatic evaluation metrics for open-domain dialogue systems correlate poorly with human evaluation. |
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