基于DPCNN和多学习模式损失的富上下文反讽识别

Context-rich Sarcasm Recognition Based on DPCNN and Multiple Learning Modes Loss

反讽作为一种层次丰富且复杂的语言表达方式,广泛存在于人们的日常表达和社交平台中。在电子商务、事件话题分析等方面,准确检测评论文本是否具有反讽意图对判断评论者情感倾向、对评论主体的好恶至关重要。研究针对会话上下文、用户上下文、主题上下文这3类反讽上下文语境,构建上下文语境丰富的反讽检测模型。针对传统浅层CNN难以捕获句子远距离依赖的问题,所提模型引入DPCNN架构捕获语句远程关联信息,并融合双向注意力机制学习会话上下文中的不协调信息。考虑到现实的数据样本中反讽类型数量少、反讽表达层次不均衡,还提出一种多学习模式的非对称损失函数,来解决样本类别不平衡、难易样本优先学习的问题。通过在3个公开反讽数据集上进行验证实验,结果表明所提模型在ACC、F1和AUC指标上均优于基准模型,最高超出2.5%。消融实验证明所提模型各个模块以及多学习模式损失函数均能提升反讽检测的性能。

As a richly layered and complex linguistic expression,sarcasm is widely observed in people’s daily expressions and social platforms,and correctly detecting whether a comment has ironic intent in e-commerce,event topic analysis,etc.,is crucial to determine a commenter’s emotional tendency,attitude to the comment subject.Three types of contexts,namely,conversation context,user context and topic context,have been covered to build a context-rich sarcasm detection model.To address the problem that traditional shallow CNNs are difficult to capture sentence long-term dependencies,the proposed model introduces the DPCNN architecture to capture utterance remote association information and incorporates the bidirectional attention mechanism to learn incongruity information in conversation context.Considering the small number of sarcasm types and unbalanced levels of sarcasm expressions in realistic data samples,an asymmetric loss function with multiple learning modes is also proposed.Experiments are conducted on three public and real sarcasm datasets,and the results demonstrate that the method in this paper outperforms the benchmark model in ACC,F1 and AUC metrics by up to 2.5%,and the effectiveness of each module of the proposed model and the loss function of the multiple learning modes is demonstrated by ablation experiments,which can improve the performance of sarcasm detection.