论文笔记-Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentimen

Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification

动机

• where dialog act and sentiment can indicate the explicit and the implicit intentions separately. SC can detect the sentiments in utterances which can help to capture speakers’implicit intentions.
  
• 认为两个信息很重要，上下文信息和交互信息，之前的方法要么考虑一个信息，要么是使用pipeline的形式，单独建模。
  

Related Work

• 1、上图a coling 2018：Multi-task dialog act and sentiment recognition on Mastodon
  • We manually annotate both dialogues and sentiments on this corpus, and train a multi-task hierarchical recurrent network – joint learning
  • can implicitly extract the shared mutual interaction information, but fail to effectively capture the contextual information

• 2、上图b PR期刊，只考虑上下文的信息 Integrated neural network model for identifying speech acts, predicators, and sentiments of dialogue utterances
  • explicitly leverage the previous act information to guide the current DA prediction
  • the model ignores the mutual interaction information

数据中的一个例子:

• 3、2020 AAAI DCR-Net
  • capture the contextual information, followed then by a relation layer to consider the mutual interaction information.
  • Pipeline way：two info model independently

Contributions

• first attempt to simultaneously incorporate contextual information and mutual interaction information
• propose a co-interactive graph attention network where a cross-tasks connection and cross-utterances connection

Method

• Speaker-Level Encoder , 使用GNN考虑同一个说话的人的信息。边，如果是同一个人，为1
• Stacked Co-Interactive Graph Layer ，2N个结点，2N*2N条边。
  • Cross-utt info
  • Cross-tasks info
    

Experiments

问题

• 本质上来说，该做法还是依赖数据中的某些规律。
• 跨任务图的计算还是一种全连接的方式，有边就是1，否则就是0；怎么更好建模一阶邻居？