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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
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Posts
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portfolio
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publications
ConvLab: Multi-Domain End-to-End Dialog System Platform
Published in ACL, 2019
Recommended citation: Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Xiang Li, Yaoqin Zhang, Zheng Zhang, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao. ConvLab: Multi-Domain End-to-End Dialog System Platform. https://arxiv.org/pdf/1904.08637
Unsupervised Dialogue Spectrum Generation for Log Dialogue Ranking
Published in SigDial, 2019
Recommended citation: Xinnuo Xu, Yizhe Zhang, Lars Liden, Sungjin Lee. Unsupervised Dialogue Spectrum Generation for Log Dialogue Ranking. https://www.aclweb.org/anthology/W19-5919.pdf
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks
Published in EMNLP, 2019
Recommended citation: Igor Shalyminov, Sungjin Lee, Arash Eshghi, Oliver Lemon. Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks. https://www.aclweb.org/anthology/D19-1183.pdf
Generating a Common Question from Multiple Documents using a MapReduce Encoder-Decoder Model
Published in EMNLP Workshop, 2019
Recommended citation: Woon Sang Cho, Yizhe Zhang, Sudha Rao, Chris Brockett, Sungjin Lee. Generating a Common Question from Multiple Documents using a MapReduce Encoder-Decoder Model. https://www.aclweb.org/anthology/D19-5604.pdf
Structuring Latent Spaces for Stylized Response Generation
Published in EMNLP, 2019
Recommended citation: Xiang Gao, Yizhe Zhang, Sungjin Lee, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan. Structuring Latent Spaces for Stylized Response Generation. https://www.aclweb.org/anthology/D19-1190.pdf
The Eighth Dialog System Technology Challenge
Published in NIPS 2019, Conversational AI, 2019
Recommended citation: Seokhwan Kim, Michel Galley, Chulaka Gunasekara, Sungjin Lee, Adam Atkinson, Baolin Peng, Hannes Schulz, Jianfeng Gao, Jinchao Li, Mahmoud Adada, Minlie Huang, Luis Lastras, Jonathan K. Kummerfeld, Walter S. Lasecki, Chiori Hori, Anoop Cherian, Tim K. Marks, Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta. The Eighth Dialog System Technology Challenge. https://arxiv.org/pdf/1911.06394
Datasets and Benchmarks for Task-Oriented Log Dialogue Ranking Task
Published in Interspeech, 2020
Recommended citation: Xinnuo Xu, Yizhe Zhang, Lars Liden and Sungjin Lee. Datasets and Benchmarks for Task-Oriented Log Dialogue Ranking Task. https://indico2.conference4me.psnc.pl/event/35/contributions/3393/attachments/773/811/Thu-1-9-8.pdf
Guided Dialog Policy Learning without Adversarial Learning in the Loop
Published in EMNLP Findings, 2020
Recommended citation: Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Shahin Shayandeh, and Jianfeng Gao. Guided Dialog Policy Learning without Adversarial Learning in the Loop. https://arxiv.org/pdf/2004.03267.pdf
Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents
Published in NeurIPS 2020, Human in the Loop Dialogue Systems Workshop, 2020
Recommended citation: Dookun Park, Hao Yuan, Dongmin Kim, Yinglei Zhang, Matsoukas Spyros, Young-Bum Kim, Ruhi Sarikaya, Edward Guo, Yuan Ling, Kevin Quinn, Pham Hung, Benjamin Yao, Sungjin Lee. Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents.' https://arxiv.org/pdf/2006.07113.pdf
A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues
Published in preprint, 2021
Recommended citation: Ziming Li, Dookun Park, Julia Kiseleva, Young-Bum Kim, Sungjin Lee. A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues. https://arxiv.org/abs/2103.01287
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration
Published in preprint, 2021
Recommended citation: Han Li, Sunghyun Park, Aswarth Dara, Jinseok Nam, Sungjin Lee, Young-Bum Kim, Spyros Matsoukas, Ruhi Sarikaya. Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration. https://arxiv.org/abs/2103.03373
Handling Long-Tail Queries with Slice-Aware Conversational Systems
Published in ICLR 2021 Workshop on Weakly Supervised Learning, 2021
Recommended citation: Cheng Wang, Sun Kim, Taiwoo Park, Sajal Choudhary, Sunghyun Park, Young-Bum Kim, Ruhi Sarikaya, Sungjin Lee. Handling Long-Tail Queries with Slice-Aware Conversational Systems. https://arxiv.org/abs/2104.13216
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents
Published in NAACL, 2021
Recommended citation: Mohammad Kachuee, Hao Yuan, Young-Bum Kim, Sungjin Lee. Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents.' https://arxiv.org/abs/2010.11230
AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation
Published in ACL, 2021
Recommended citation: Xinnuo Xu, Guoyin Wang, Young-Bum Kim, Sungjin Lee. AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation. https://arxiv.org/abs/2106.05589
Learning Slice-Aware Representations with Mixture of Attentions
Published in ACL Findings, 2021
Recommended citation: Cheng Wang, Sungjin Lee, Sunghyun Park, Han Li, Young-Bum Kim, Ruhi Sarikaya. Learning Slice-Aware Representations with Mixture of Attentions. https://arxiv.org/abs/2106.02363
A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems
Published in EMNLP, 2021
Recommended citation: Sunghyun Park, Han Li, Ameen Patel, Sidharth Mudgal, Sungjin Lee, Young-Bum Kim, Spyros Matsoukas, Ruhi Sarikaya. A Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems.' https://arxiv.org/abs/2010.12251
talks
Towards Large-scale Federated Conversational Intelligence
Published:
Conversational agents have become prevalent in every aspect of our lives. Conversational agents such as Alexa and Google Assistant are no longer closed applications but rather they have evolved to be an ecosystem featuring hundreds of thousands of voice skills and offer a rich set of tools to bring in unlimited number of skills, for instance, an AI-centric toolkit for voice skill authoring, seamless cold start of new skills, and traffic optimization based on user satisfaction. In this talk, I discuss recent industrial trends towards large-scale federated conversational intelligence and give a sneak peek of present challenges and approaches in industry.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.