TY - GEN
T1 - Generating Informative Dialogue Responses with Keywords-Guided Networks
AU - Xu, Heng Da
AU - Mao, Xian Ling
AU - Chi, Zewen
AU - Sun, Fanshu
AU - Zhu, Jingjing
AU - Huang, Heyan
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Recently, open-domain dialogue systems have attracted growing attention. Most of them use the sequence-to-sequence (Seq2Seq) architecture to generate dialogue responses. However, traditional Seq2Seq-based open-domain dialogue models tend to generate generic and safe responses, which are less informative, unlike human responses. In this paper, we propose a simple but effective Keywords-guided Sequence-to-sequence model (KW-Seq2Seq) which uses keywords information as guidance to generate open-domain dialogue responses. Specifically, given the dialogue context, KW-Seq2Seq first uses a keywords decoder to predict a sequence of topic keywords, and then generates the final response under the guidance of them. Extensive experiments demonstrate that the keywords information can facilitate the model to produce more informative, coherent, and fluent responses, yielding substantive gain in both automatic and human evaluation metrics.
AB - Recently, open-domain dialogue systems have attracted growing attention. Most of them use the sequence-to-sequence (Seq2Seq) architecture to generate dialogue responses. However, traditional Seq2Seq-based open-domain dialogue models tend to generate generic and safe responses, which are less informative, unlike human responses. In this paper, we propose a simple but effective Keywords-guided Sequence-to-sequence model (KW-Seq2Seq) which uses keywords information as guidance to generate open-domain dialogue responses. Specifically, given the dialogue context, KW-Seq2Seq first uses a keywords decoder to predict a sequence of topic keywords, and then generates the final response under the guidance of them. Extensive experiments demonstrate that the keywords information can facilitate the model to produce more informative, coherent, and fluent responses, yielding substantive gain in both automatic and human evaluation metrics.
KW - Dialogue system
KW - Keywords-guided networks
KW - Response generation
UR - http://www.scopus.com/inward/record.url?scp=85118194748&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-88483-3_14
DO - 10.1007/978-3-030-88483-3_14
M3 - Conference contribution
AN - SCOPUS:85118194748
SN - 9783030884826
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 179
EP - 192
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
T2 - 10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021
Y2 - 13 October 2021 through 17 October 2021
ER -