A part-of-speech enhanced neural conversation model

Chuwei Luo, Wenjie Li, Qiang Chen, Yanxiang He

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Modeling syntactic information of sentences is essential for neural response generation models to produce appropriate response sentences of high linguistic quality. However, no previous work in conversational responses generation using sequence-to-sequence (Seq2Seq) neural network models has reported to take the sentence syntactic information into account. In this paper, we present two part-of-speech (POS) enhanced models that incorporate the POS information into the Seq2Seq neural conversation model. When training these models, corresponding POS tag is attached to each word in the post and the response so that the word sequences and the POS tag sequences can be interrelated. By the time the word in a response is to be generated, it is constrained by the expected POS tag. The experimental results show that the POS enhanced Seq2Seq models can generate more grammatically correct and appropriate responses in terms of both perplexity and BLEU measures when compared with the word Seq2Seq model.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319566078
Publication statusPublished - 1 Jan 2017
Event39th European Conference on Information Retrieval, ECIR 2017 - Aberdeen, United Kingdom
Duration: 8 Apr 201713 Apr 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10193 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference39th European Conference on Information Retrieval, ECIR 2017
Country/TerritoryUnited Kingdom


  • Response generation
  • Seq2Seq neural conversation model
  • Syntactic information incorporating

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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