Zobrazeno 1 - 10
of 230
pro vyhledávání: '"Kim, Seokhwan"'
Information-Seeking Dialogue (ISD) agents aim to provide accurate responses to user queries. While proficient in directly addressing user queries, these agents, as well as LLMs in general, predominantly exhibit reactive behavior, lacking the ability
Externí odkaz:
http://arxiv.org/abs/2410.15297
Autor:
Zhao, Chao, Gella, Spandana, Kim, Seokhwan, Jin, Di, Hazarika, Devamanyu, Papangelis, Alexandros, Hedayatnia, Behnam, Namazifar, Mahdi, Liu, Yang, Hakkani-Tur, Dilek
Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely on domain-specific APIs/DBs or external factual knowledge to generate
Externí odkaz:
http://arxiv.org/abs/2305.12091
Autor:
Lin, Yen-Ting, Papangelis, Alexandros, Kim, Seokhwan, Lee, Sungjin, Hazarika, Devamanyu, Namazifar, Mahdi, Jin, Di, Liu, Yang, Hakkani-Tur, Dilek
This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLM
Externí odkaz:
http://arxiv.org/abs/2302.05096
Autor:
Chen, Maximillian, Papangelis, Alexandros, Tao, Chenyang, Kim, Seokhwan, Rosenbaum, Andy, Liu, Yang, Yu, Zhou, Hakkani-Tur, Dilek
Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising direction to tackle this problem is to generate synthetic dialogues by prompti
Externí odkaz:
http://arxiv.org/abs/2302.03269
Autor:
Chen, Maximillian, Papangelis, Alexandros, Tao, Chenyang, Rosenbaum, Andy, Kim, Seokhwan, Liu, Yang, Yu, Zhou, Hakkani-Tur, Dilek
Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier, we explore few-shot data augmentation for dialogue understanding by p
Externí odkaz:
http://arxiv.org/abs/2210.14169
While rich, open-domain textual data are generally available and may include interesting phenomena (humor, sarcasm, empathy, etc.) most are designed for language processing tasks, and are usually in a non-conversational format. In this work, we take
Externí odkaz:
http://arxiv.org/abs/2207.11363
In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions. This work focuses on a challenging case of OOD detection, where no labels for in-domain data are a
Externí odkaz:
http://arxiv.org/abs/2203.11396
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation
Autor:
Zhou, Pei, Gopalakrishnan, Karthik, Hedayatnia, Behnam, Kim, Seokhwan, Pujara, Jay, Ren, Xiang, Liu, Yang, Hakkani-Tur, Dilek
Implicit knowledge, such as common sense, is key to fluid human conversations. Current neural response generation (RG) models are trained to generate responses directly, omitting unstated implicit knowledge. In this paper, we present Think-Before-Spe
Externí odkaz:
http://arxiv.org/abs/2110.08501
Rich, open-domain textual data available on the web resulted in great advancements for language processing. However, while that data may be suitable for language processing tasks, they are mostly non-conversational, lacking many phenomena that appear
Externí odkaz:
http://arxiv.org/abs/2110.08383
Autor:
Santhanam, Sashank, Hedayatnia, Behnam, Gella, Spandana, Padmakumar, Aishwarya, Kim, Seokhwan, Liu, Yang, Hakkani-Tur, Dilek
Recently neural response generation models have leveraged large pre-trained transformer models and knowledge snippets to generate relevant and informative responses. However, this does not guarantee that generated responses are factually correct. In
Externí odkaz:
http://arxiv.org/abs/2110.05456