Zobrazeno 1 - 10
of 669
pro vyhledávání: '"Kim, Young Bum"'
Autor:
Go, Dongyoung, Whang, Taesun, Lee, Chanhee, Kim, Hwa-Yeon, Park, Sunghoon, Ji, Seunghwan, Kim, Jinho, Kim, Dongchan, Kim, Young-Bum
The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language Models (MLLMs) has revolutionized information retrieval and expanded the practical applications of AI. However, current systems struggle in accurately interpreting
Externí odkaz:
http://arxiv.org/abs/2411.12287
A large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity. When ambiguities are detected, the agent should engage in a clarifying dialog
Externí odkaz:
http://arxiv.org/abs/2109.12451
Autor:
da Cruz Rodrigues, Kellen Cristina, Kim, Seung Chan, Uner, Aaron Aykut, Hou, Zhi-Shuai, Young, Jennie, Campolim, Clara, Aydogan, Ahmet, Chung, Brendon, Choi, Anthony, Yang, Won-Mo, Kim, Woojin S., Prevot, Vincent, Caldarone, Barbara J., Lee, Hyon, Kim, Young-Bum
Publikováno v:
In Molecular Metabolism June 2024 84
Publikováno v:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL2021)
Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For large-scale conversational systems, where it is common to have over hundre
Externí odkaz:
http://arxiv.org/abs/2106.05589
Real-world machine learning systems are achieving remarkable performance in terms of coarse-grained metrics like overall accuracy and F-1 score. However, model improvement and development often require fine-grained modeling on individual data subsets
Externí odkaz:
http://arxiv.org/abs/2106.02363
Autor:
Wang, Cheng, Kim, Sun, Park, Taiwoo, Choudhary, Sajal, Park, Sunghyun, Kim, Young-Bum, Sarikaya, Ruhi, Lee, Sungjin
We have been witnessing the usefulness of conversational AI systems such as Siri and Alexa, directly impacting our daily lives. These systems normally rely on machine learning models evolving over time to provide quality user experience. However, the
Externí odkaz:
http://arxiv.org/abs/2104.13216
Autor:
Li, Han, Park, Sunghyun, Dara, Aswarth, Nam, Jinseok, Lee, Sungjin, Kim, Young-Bum, Matsoukas, Spyros, Sarikaya, Ruhi
Current state-of-the-art large-scale conversational AI or intelligent digital assistant systems in industry comprises a set of components such as Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). For some of these systems t
Externí odkaz:
http://arxiv.org/abs/2103.03373
Digital assistants are experiencing rapid growth due to their ability to assist users with day-to-day tasks where most dialogues are happening multi-turn. However, evaluating multi-turn dialogues remains challenging, especially at scale. We suggest a
Externí odkaz:
http://arxiv.org/abs/2103.01287
Autor:
Park, Sunghyun, Li, Han, Patel, Ameen, Mudgal, Sidharth, Lee, Sungjin, Kim, Young-Bum, Matsoukas, Spyros, Sarikaya, Ruhi
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request. We propose a scalable and automatic approach for im
Externí odkaz:
http://arxiv.org/abs/2010.12251
Turn-level user satisfaction is one of the most important performance metrics for conversational agents. It can be used to monitor the agent's performance and provide insights about defective user experiences. Moreover, a powerful satisfaction model
Externí odkaz:
http://arxiv.org/abs/2010.11230