Discovering Dialog Structure Graph for Coherent Dialog Generation (2021)

Abstract: Learning discrete dialog structure graph from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation. However, this problem is less studied in open-domain dialogue. In this paper, we conduct unsupervised discovery of discrete dialog structure from chitchat corpora, and then leverage it to facilitate coherent dialog generation in downstream systems. To this end, we present an unsupervised model, Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover discrete hierarchical latent dialog states (at the level of both session and utterance) and their transitions from corpus as a dialog structure graph. Then we leverage it as background knowledge to facilitate dialog management in a RL based dialog system. Experimental results on two benchmark corpora confirm that DVAE-GNN can discover meaningful dialog structure graph, and the use of dialog structure as background knowledge can significantly improve multi-turn coherence.

## introduction with the aim of building a machine to converse with humans naturally, some work investigate neural generative models @xcite . while these models can generate locally relevant dialogs, they struggle to organize individual utterances into globally coherent flow @xcite . the possible reason is that it is difficult to control the overall dialog flow without background knowledge about dialog structure. 1 however, due to the complexity of opendomain conversation, it is laborious and costly to annotate dialog structure manually. therefore, it is of great importance to discover open-domain dialog structure from corpus in an unsupervised way for coherent dialog generation. some studies tried to discover dialog structure from task-oriented dialogs @xcite . however, the number of their dialog states is limited to only dozens or hundreds, which cannot cover finegrained semantics in open-domain dialogs. furthermore, the dialog structures they discovered generally only contain utterance-level semantics (non-hierarchical), without session-level semantics (chatting topics) that are essential in open-domain dialogs @xcite @xcite ). 2 thus, in order to provide a full picture of open-domain dialog structure, it is desirable to discover a two-layer directed graph that contains session-level semantics in the upper-layer vertices, utterance-level semantics in the lower-layer vertices, and edges among these vertices. in this paper, we propose a novel discrete variational auto-encoder with graph neural network (dvae-gnn) to discover a two-layer dialog structure from chitchat corpus. intuitively, since discrete dialog states are easier to capture transitions for dialog coherence, we use discrete variables to represent dialog states (or vertices in the graph) rather than dense continuous ones in most vaebased dialog models @xcite . specifically, we employ an rnn encoder with softmax function as vertex recognition module in dvae, and an rnn decoder as reconstruction module in dvae, as shown in figure 3 . furthermore, we integrate gnn into dvae to model complex relations among discrete variables for more effective discovery. the parameters of dvae-gnn can be optimized by minimizing a reconstruction loss, without the requirement of any annotated datasets. to prove the effectiveness of the discovered structure, we propose a hierarchical reinforcement learning (rl) based graph grounded conversational system (gcs) to leverage it for conversation generation. as shown in figure 2 , given a dialog context, gcs first maps it to a utterance-level vertex, and then learns to walk over graph edges, and finally selects a contextual appropriate utterance-level vertex to guide response generation at each turn. our contribution includes: (1) we identify the task of unsupervised dialog structure graph discovery in open-domain dialogs. (2) we propose a novel model, dvae-gnn, for hierarchical dialog struc3 co-occurrence means that two utterance-level vertices are mapped by two adjacent utterances in a session. ture graph discovery. experimental results on two benchmark corpora demonstrate that we can discover meaningful dialog structure, the use of gnn is crucial to dialog structure discovery, and the graph can improve dialog coherence significantly. ## experiments for graph grounded dialog generation to confirm the benefits of discovered dialog structure graph for coherent conversation generation, we conduct experiments on the graph discovered from weibo corpus. all the systems (including baselines) are trained on weibo corpus. ## conclusion in this paper, we conduct unsupervised discovery of discrete dialog structure from chitchat corpora. further, we try to formalize the structure as a twolayer directed graph. to discover the dialog structure, we present an unsupervised model, dvae-gnn, which integrates gnn into dvae to model complex relations among dialog states for more effective dialog structure discovery. experimental results demonstrate that dvae-gnn can discover meaningful dialog structure, and the use of dialog structure as background knowledge can significantly improve multi-turn dialog coherence. tiancheng zhao, kyusong lee, and maxine eskenazi. 2018. unsupervised discrete sentence representation learning for interpretable neural dialog generation. in proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers), pages 1098-1107, melbourne, australia. association for computational linguistics. tiancheng zhao, ran zhao, and maxine eskenazi. 2017. learning discourse-level diversity for neural dialog models using conditional variational autoencoders. in proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: long papers), pages 654-664, vancouver, canada. association for computational linguistics. xiangyang zhou, lu li, daxiang dong, yi liu, ying chen, wayne xin zhao, dianhai yu, and hua wu. 2018. multi-turn response selection for chatbots with deep attention matching network. in proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers), pages 1118-1127, melbourne, australia. association for computational linguistics.