Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Tang, Chengguang"'
Autor:
Zhang, Chen, Chong, Dading, Jiang, Feng, Tang, Chengguang, Gao, Anningzhe, Tang, Guohua, Li, Haizhou
In natural human-to-human conversations, participants often receive feedback signals from one another based on their follow-up reactions. These reactions can include verbal responses, facial expressions, changes in emotional state, and other non-verb
Externí odkaz:
http://arxiv.org/abs/2409.13948
Autor:
Zhang, Chen, Tang, Chengguang, Chong, Dading, Shi, Ke, Tang, Guohua, Jiang, Feng, Li, Haizhou
Mainstream approaches to aligning large language models (LLMs) heavily rely on human preference data, particularly when models require periodic updates. The standard process for iterative alignment of LLMs involves collecting new human feedback for e
Externí odkaz:
http://arxiv.org/abs/2405.20215
Recent advancements in reference-free learned metrics for open-domain dialogue evaluation have been driven by the progress in pre-trained language models and the availability of dialogue data with high-quality human annotations. However, current stud
Externí odkaz:
http://arxiv.org/abs/2310.08958
Autor:
Rodríguez-Cantelar, Mario, Zhang, Chen, Tang, Chengguang, Shi, Ke, Ghazarian, Sarik, Sedoc, João, D'Haro, Luis Fernando, Rudnicky, Alexander
The advent and fast development of neural networks have revolutionized the research on dialogue systems and subsequently have triggered various challenges regarding their automatic evaluation. Automatic evaluation of open-domain dialogue systems as a
Externí odkaz:
http://arxiv.org/abs/2306.12794
Autor:
Zhang, Zhenyu, Yu, Bowen, Yu, Haiyang, Liu, Tingwen, Fu, Cheng, Li, Jingyang, Tang, Chengguang, Sun, Jian, Li, Yongbin
Building document-grounded dialogue systems have received growing interest as documents convey a wealth of human knowledge and commonly exist in enterprises. Wherein, how to comprehend and retrieve information from documents is a challenging research
Externí odkaz:
http://arxiv.org/abs/2207.06717
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding
Autor:
Zhang, Taolin, Wang, Chengyu, Hu, Nan, Qiu, Minghui, Tang, Chengguang, He, Xiaofeng, Huang, Jun
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities. To guarantee effective knowledge injection, previous studies integrate m
Externí odkaz:
http://arxiv.org/abs/2112.01047
Autor:
Niu, Guanglin, Li, Yang, Tang, Chengguang, Hu, Zhongkai, Yang, Shibin, Li, Peng, Wang, Chengyu, Wang, Hao, Sun, Jian
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer. Recent approaches attempt to introduce the knowledge graph embedding (KGE) technique to handle the
Externí odkaz:
http://arxiv.org/abs/2110.15622
Autor:
Niu, Guanglin, Li, Yang, Tang, Chengguang, Geng, Ruiying, Dai, Jian, Liu, Qiao, Wang, Hao, Sun, Jian, Huang, Fei, Si, Luo
Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing models employ a few-shot relation's multi-hop neighbor information to en
Externí odkaz:
http://arxiv.org/abs/2104.13095
Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge bases. In order to make KBQA more applicable in actual scenarios, r
Externí odkaz:
http://arxiv.org/abs/2007.13069
Dialog management (DM) is a crucial component in a task-oriented dialog system. Given the dialog history, DM predicts the dialog state and decides the next action that the dialog agent should take. Recently, dialog policy learning has been widely for
Externí odkaz:
http://arxiv.org/abs/2005.02233