Generating Contextually Coherent Responses by Learning Structured Vectorized Semantics

Yan Wang, Yanan Zheng, Shimin Jiang, Yucheng Dong, Jessica Chen, Shaohua Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Generating contextually coherent responses has been one of the most critical challenges in building intelligent dialogue systems. Key issues are how to appropriately encode contexts and how to make good use of them during the generation. Past works either directly use (hierarchical) RNN to encode contexts or use attention-based variants to further weight different words and utterances. They tend to learn dispersed focuses over all contextual information, which contradicts the facts that humans tend to respond to certain concentrated semantics of contexts. This leads to the results that generated responses are only show semantically related to, but not precisely coherent with the given contexts. To this end, this paper proposes a contextually coherent dialogue generation (ConDial) method by first encoding contexts into structured semantic vectors using self-attention, and then adaptively choosing key semantic vectors to guide the response generation. Based on the structured semantics, it also develops a calibration mechanism with a dynamic vocabulary during decoding, which enhances exact coherent expressions by adjusting word distribution. According to the experiments, ConDial shows better generative performance than state-of-the-arts and is capable of generating responses that not only continue the topics but also keep coherent contextual expressions.

Original languageEnglish (US)
Title of host publicationDatabase Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings
EditorsChristian S. Jensen, Ee-Peng Lim, De-Nian Yang, Chia-Hui Chang, Jianliang Xu, Wen-Chih Peng, Jen-Wei Huang, Chih-Ya Shen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783030731960
StatePublished - 2021
Event26th International Conference on Database Systems for Advanced Applications, DASFAA 2021 - Taipei, Taiwan, Province of China
Duration: Apr 11 2021Apr 14 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12682 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Conference on Database Systems for Advanced Applications, DASFAA 2021
Country/TerritoryTaiwan, Province of China

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Calibration mechanism
  • Contextual coherence
  • Dialogue generation
  • Structured vectorized semantics


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