TY - GEN
T1 - MSDF
T2 - 10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021
AU - Zhao, Yu
AU - Hu, Xinshuo
AU - Li, Yunxin
AU - Hu, Baotian
AU - Li, Dongfang
AU - Chen, Sichao
AU - Wang, Xiaolong
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Dialog systems have achieved significant progress and have been widely used in various scenarios. The previous researches mainly focused on designing dialog generation models in a single scenario, while comprehensive abilities are required to handle tasks under various scenarios in the real world. In this paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which can be applied in different dialog tasks (e.g. knowledge grounded dialog and persona based dialog). Specifically, we propose a transferable response generator pre-trained on diverse large-scale dialog corpora as the backbone of MSDF, consisting of BERT-based encoders and a GPT-based decoder. To select the response consistent with dialog history, we propose a consistency selector trained through negative sampling. Moreover, the flexible copy mechanism of external knowledge is also employed to enhance the utilization of multiform knowledge in various scenarios. We conduct experiments on knowledge grounded dialog, recommendation dialog, and persona based dialog tasks. The experimental results indicate that our MSDF outperforms the baseline models with a large margin. In the Multi-skill Dialog of 2021 Language and Intelligence Challenge, our general MSDF won the 3rd prize, which proves our MSDF is effective and competitive.
AB - Dialog systems have achieved significant progress and have been widely used in various scenarios. The previous researches mainly focused on designing dialog generation models in a single scenario, while comprehensive abilities are required to handle tasks under various scenarios in the real world. In this paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which can be applied in different dialog tasks (e.g. knowledge grounded dialog and persona based dialog). Specifically, we propose a transferable response generator pre-trained on diverse large-scale dialog corpora as the backbone of MSDF, consisting of BERT-based encoders and a GPT-based decoder. To select the response consistent with dialog history, we propose a consistency selector trained through negative sampling. Moreover, the flexible copy mechanism of external knowledge is also employed to enhance the utilization of multiform knowledge in various scenarios. We conduct experiments on knowledge grounded dialog, recommendation dialog, and persona based dialog tasks. The experimental results indicate that our MSDF outperforms the baseline models with a large margin. In the Multi-skill Dialog of 2021 Language and Intelligence Challenge, our general MSDF won the 3rd prize, which proves our MSDF is effective and competitive.
KW - Conversational recommendation
KW - Knowledge grounded dialog
KW - Multi-skill dialog
KW - Persona based dialog
UR - https://www.scopus.com/pages/publications/85118145940
U2 - 10.1007/978-3-030-88483-3_29
DO - 10.1007/978-3-030-88483-3_29
M3 - 会议稿件
AN - SCOPUS:85118145940
SN - 9783030884826
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 365
EP - 376
BT - Natural Language Processing and Chinese Computing - 10th CCF International Conference, NLPCC 2021, Proceedings
A2 - Wang, Lu
A2 - Feng, Yansong
A2 - Hong, Yu
A2 - He, Ruifang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 13 October 2021 through 17 October 2021
ER -