Year: 2023
Type: preprint
Abstract: In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial. However, domain-specific conversational dialogue systems may be incoherent and introduce erroneous external information to answer questions due to the out-of-vocabulary issue or the wrong knowledge from the parameters of the neural network. In this work, we propose PK-Chat, a Pointer network guided Knowledge-driven generative dialogue model, incorporating a unified pretrained language model and a pointer network over knowledge graphs. The words generated by PK-Chat in the dialogue are derived from the prediction of word lists and the direct prediction of the external knowledge graph knowledge. Moreover, based on the PK-Chat, a dialogue system is built for academic scenarios in the case of geosciences. Finally, an academic dialogue benchmark is constructed to evaluate the quality of dialogue systems in academic scenarios and the source code is available online.
Source: arXiv (Cornell University)
Authors: Deng, Cheng, Tong, Bo, Fu, Luoyi, Ding, Jiaxin, Cao, Dexing, Wang, Xinbing, Zhou, Chenghu
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4362597759