Directed Acyclic Graph Network for Conversational Emotion Recognition (2021)

Abstract: The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.

## introduction utterance-level emotion recognition in conversation (erc) is an emerging task that aims to identify the emotion of each utterance in a conversation. this task has been recently concerned by a considerable number of nlp researchers due to its potential applications in several areas, such as opinion mining in social media @xcite and building an emotional and empathetic dialog system @xcite . the emotion of a query utterance is likely to be influenced by many factors such as the utterances spoken by the same speaker and the surrounding conversation context. indeed, how to model the conversational context lies at the heart of this task @xcite . empirical evidence also shows that a good representation of conversation context significantly contributes to the model performance, especially when the content of query utterance is too short to be identified alone @xcite . numerous efforts have been devoted to the modeling of conversation context. basically, they can be divided into two categories: graph-based methods @xcite @xcite @xcite and recurrence-based methods @xcite @xcite . for the graphbased methods, they concurrently gather information of the surrounding utterances within a certain window, while neglecting the distant utterances and the sequential information. for the recurrencebased methods, they consider the distant utterances and sequential information by encoding the utterances temporally. however, they tend to update the query utterance's state with only relatively limited information from the nearest utterances, making them difficult to get a satisfying performance. according to the above analysis, an intuitively better way to solve erc is to allow the advantages of graph-based methods and recurrence-based models to complement each other. this can be achieved by regarding each conversation as a directed acyclic graph (dag). as illustrated in figure 1 , each utterance in a conversation only receives information from some previous utterances and cannot propagate information backward to itself and its predecessors through any path. this characteristic indicates that a conversation can be regarded as a dag. moreover, by the information flow from predecessors to successors through edges, dag can gather information for a query utterance from both the neighboring utterances and the remote utterances, which acts like a combination of graph structure and recurrence structure. thus, we speculate that dag is a more appropriate and reasonable way than graph-based structure and recurrence-based structure to model the conversation context in erc. in this paper, we propose a method to model the conversation context in the form of dag. firstly, rather than simply connecting each utterance with a fixed number of its surrounding utterances to build a graph, we propose a new way to build a dag from the conversation with constraints on speaker identity and positional relations. secondly, inspired by dagnn @xcite , we propose a directed acyclic graph neural network for erc, namely dag-erc. unlike the traditional graph neural networks such as @xcite and gat @xcite ) that aggregate information from the previous layer, dag-erc can recurrently gather information of predecessors for every utterance in a single layer, which enables the model to encode the remote context without having to stack too many layers. besides, in order to be more applicable to the erc task, our dag-erc has two improvements over dagnn: the contributions of this paper are threefold. first, we are the first to consider a conversation as a directed acyclic graph in the erc task. second, we propose a method to build a dag from a conversation with constraints based on the speaker identity and positional relations. third, we propose a directed acyclic graph neural network for erc, which takes dagnn as its backbone and has two main improvements designed specifically for erc. ## conclusion in this paper, we presented a new idea of modeling conversation context with a directed acyclic graph (dag) and proposed a directed acyclic graph neural network, namely dag-erc, for emotion recognition in conversation (erc). extensive experiments were conducted and the results show that the proposed dag-erc achieves comparable performance with the baselines. moreover, by comprehensive evaluations and ablation study, we confirmed the superiority of our dag-erc and the impact of its modules. several conclusions can be drawn from the empirical results. first, the dag structures built from conversations do affect the performance of dag-erc, and with the constraints on speaker identity and positional relation, the proposed dag structure outperforms its variants. sec-ond, the widely utilized graph relation type of whether two utterances have the same speaker is insufficient for multi-speaker conversations. third, the directed acyclic graph network does not suffer over-smoothing as easily as gnns when the number of layers increases. finally, many of the errors misjudged by dag-erc can be accounted for by similar emotions, neutral samples and emotional shift. these reasons have been partly mentioned in previous works but have yet to be solved, which are worth further investigation in future work.