A Quantum Probability Inspired Network for Dialogue Sarcasm Recognition

ZHANG Ya-zhou; YU Yang; ZHU Shao-lin; CHEN Rui; RONG Lu; LIANG Hui

doi:10.12263/DZXB.20211075

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A Quantum Probability Inspired Network for Dialogue Sarcasm Recognition

PAPERS | 更新时间：2025-12-08

- A Quantum Probability Inspired Network for Dialogue Sarcasm Recognition
- ACTA ELECTRONICA SINICA Vol. 50, Issue 8, Pages: 1885-1893(2022)
- 作者机构：
  
  郑州轻工业大学软件学院，河南郑州 450002
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20211075
  CLC： TP391
- Received：11 August 2021，
  
  Revised：2022-04-11，
  
  Published：25 August 2022
- 稿件说明：
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张亚洲,俞洋,朱少林等.一种量子概率启发的对话讽刺识别网络模型[J].电子学报,2022,50(08):1885-1893.

ZHANG Ya-zhou,YU Yang,ZHU Shao-lin,et al.A Quantum Probability Inspired Network for Dialogue Sarcasm Recognition[J].ACTA ELECTRONICA SINICA,2022,50(08):1885-1893.
张亚洲,俞洋,朱少林等.一种量子概率启发的对话讽刺识别网络模型[J].电子学报,2022,50(08):1885-1893. DOI： 10.12263/DZXB.20211075.

ZHANG Ya-zhou,YU Yang,ZHU Shao-lin,et al.A Quantum Probability Inspired Network for Dialogue Sarcasm Recognition[J].ACTA ELECTRONICA SINICA,2022,50(08):1885-1893. DOI： 10.12263/DZXB.20211075.

摘要

对话讽刺识别已经成为人工智能领域中一项极具挑战性的课题，其目的是辨别互动对话中晦涩难懂的诸如讽刺、轻蔑、嘲笑等隐喻性情感.从语言哲学分析，目前的对话讽刺识别方法难以衡量人类语言在讽刺表达与理解方面固有的不确定性.鉴于量子概率在建模不确定性方面的优势，本文探索量子概率在讽刺识别领域的潜力并提出一种量子概率启发式网络.该网络主要包含复值嵌入层、量子复合层、量子测量层以及全连接层.本文将互动对话中每句话语视作是一组单词的类量子叠加，表征为复数向量.相邻话语之间的上下文交互被建模为量子系统与其周围环境的复合，表示为密度矩阵.本文对每句话语进行量子测量，提取讽刺特征，并将讽刺特征输入到全连接层预测得到讽刺识别结果.本文在两个基准数据集上进行实验，结果表明本文提出的模型优于先进讽刺识别模型，讽刺识别准确率分别提升5.2%与2.38%.

Abstract

Dialogue sarcasm recognition has been a challenging artificial intelligence(AI) research topic

aiming to discover elusive ironic

contemptuous and metaphoric information implied in daily dialogue. From the perspective of emotional logic

most existing works are insufficient to measure the intrinsic uncertainty in emotional expression and understanding. In view of the advantages of quantum probability(QP) in modeling the uncertainty

this paper explores the potential of QP in dialogue sarcasm recognition and proposes a quantum probability inspired network(QPIN). Specially

QPIN consists of a complex-valued embedding layer

a quantum composition layer

a quantum measurement layer and a dense layer. Each utterance is treated as a quantum superposition-like of a set of basis words

using a complex-valued representation. The contextual interaction between adjacent utterances is described as the composition system between a quantum system and its surrounding environment

which is represented by the density matrix. A quantum measurement is performed on the density matrix of each utterance to extract sarcastic features

and thus feeds these features to a dense layer to yield the probabilistic outcomes. Extensive experiments are conducted on two benchmark datasets

and the results show that our model outperforms the state-of-the-art baselines

with accuracy scores enhanced by 5.2% and 2.38%

respectively.

关键词

Keywords

references

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