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pro vyhledávání: '"Finch, P. E."'
Autor:
Finch, Sarah E., Choi, Jinho D.
Open-domain dialogue systems need to grasp social commonsense to understand and respond effectively to human users. Commonsense-augmented dialogue models have been proposed that aim to infer commonsense knowledge from dialogue contexts in order to im
Externí odkaz:
http://arxiv.org/abs/2406.09138
Autor:
Finch, Sarah E., Choi, Jinho D.
Mastering commonsense understanding and reasoning is a pivotal skill essential for conducting engaging conversations. While there have been several attempts to create datasets that facilitate commonsense inferences in dialogue contexts, existing data
Externí odkaz:
http://arxiv.org/abs/2401.15471
Human evaluation has been widely accepted as the standard for evaluating chat-oriented dialogue systems. However, there is a significant variation in previous work regarding who gets recruited as evaluators. Evaluator groups such as domain experts, u
Externí odkaz:
http://arxiv.org/abs/2309.07998
Developing high-performing dialogue systems benefits from the automatic identification of undesirable behaviors in system responses. However, detecting such behaviors remains challenging, as it draws on a breadth of general knowledge and understandin
Externí odkaz:
http://arxiv.org/abs/2309.06490
Despite tremendous advancements in dialogue systems, stable evaluation still requires human judgments producing notoriously high-variance metrics due to their inherent subjectivity. Moreover, methods and labels in dialogue evaluation are not fully st
Externí odkaz:
http://arxiv.org/abs/2212.09180
Improving user experience of a dialogue system often requires intensive developer effort to read conversation logs, run statistical analyses, and intuit the relative importance of system shortcomings. This paper presents a novel approach to automated
Externí odkaz:
http://arxiv.org/abs/2111.00572
Autor:
Finch, Sarah E., Finch, James D., Huryn, Daniil, Hutsell, William, Huang, Xiaoyuan, He, Han, Choi, Jinho D.
We present a chatbot implementing a novel dialogue management approach based on logical inference. Instead of framing conversation a sequence of response generation tasks, we model conversation as a collaborative inference process in which speakers s
Externí odkaz:
http://arxiv.org/abs/2111.00570
Autor:
Finch, Sarah E., Finch, James D., Ahmadvand, Ali, Ingyu, Choi, Dong, Xiangjue, Qi, Ruixiang, Sahijwani, Harshita, Volokhin, Sergey, Wang, Zihan, Wang, Zihao, Choi, Jinho D.
Inspired by studies on the overwhelming presence of experience-sharing in human-human conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the current field of conversational AI.
Externí odkaz:
http://arxiv.org/abs/2009.04617
Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols
Autor:
Finch, Sarah E., Choi, Jinho D.
As conversational AI-based dialogue management has increasingly become a trending topic, the need for a standardized and reliable evaluation procedure grows even more pressing. The current state of affairs suggests various evaluation protocols to ass
Externí odkaz:
http://arxiv.org/abs/2006.06110
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