Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations (2023)

Abstract: Unlike empathetic dialogues, the system in emotional support conversations (ESC) is expected to not only convey empathy for comforting the help-seeker, but also proactively assist in exploring and addressing their problems during the conversation. In this work, we study the problem of mixed-initiative ESC where the user and system can both take the initiative in leading the conversation. Specifically, we conduct a novel analysis on mixed-initiative ESC systems with a tailor-designed schema that divides utterances into different types with speaker roles and initiative types. Four emotional support metrics are proposed to evaluate the mixed-initiative interactions. The analysis reveals the necessity and challenges of building mixed-initiative ESC systems. In the light of this, we propose a knowledge-enhanced mixed-initiative framework (KEMI) for ESC, which retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses. Experimental results on two ESC datasets show the superiority of KEMI in both content-preserving evaluation and mixed initiative related analyses.

## introduction as the world is making efforts to recover from covid-19 and plans for future construction, emotional support is of great importance in resolving the widespread emotional distress and increased risk for psychiatric illness associated with the pandemic @xcite . a wide range of emotional support conversation (esc) systems are emerging to provide prompt and convenient emotional support for helpseekers, including mental health support (sharma et @xcite , counseling @xcite @xcite or motivational interviewing @xcite @xcite . generally, the esc system aims at reducing the user's emotional distress as well as assisting the user to identify and overcome the problem via conversations @xcite . mixed initiative is commonly defined as an intrinsic feature of human-ai interactions where the user and the system can both take the initiative in leading the interaction directions @xcite . for example, mixedinitiative conversational information-seeking (cis) systems @xcite can proactively initiate clarification interactions for resolving the ambiguity in the user query, instead of only reacting to the query. accordingly, a mixedinitiative esc system can proactively switch the initiative to provide an empathetic response or initiate a problem-solving discussion when appropriate. many efforts have been made on the emotion reasoning for generating empathetic responses @xcite @xcite ). another line of work focuses on identifying the dialogue acts of the utterances @xcite @xcite or predicting the next conversational strategies @xcite @xcite in esc systems. however, the feature of mixed initiative has not been investigated in existing esc studies. to facilitate the analysis on mixed-initiative esc systems, we first propose an eafr schema to annotate the utterances into different types with speaker roles and initiative types, named expression (userinitiative), action (support-initiative), feedback (user non-initiative), and reflection (system noninitiative). besides, four emotional support metrics are designed to measure the characteristics of initiative and non-initiative interactions in esc, including proactivity, information, repetition, and relaxation. to analyze the necessity of considering mixed initiative in esc systems, we conduct a preliminary analysis on the different interaction patterns between esc and empathetic dialogues (ed). firstly, the dialogue flow analysis shows that the system in ed generally serves as a passive role, while the system in esc proactively switches the initiative role during the conversation. as shown in figure 1 , the system in ed solely targets at comforting the user by reflecting their feelings or echoing their situations, i.e., non-initiative. differently, esc systems are further expected to proactively explore the user's problem by asking clarifying questions and help the user overcome the problem by providing useful information or supportive suggestions, i.e., initiative. furthermore, the analysis of the conversation progress and the emotional support metrics reveal three challenges in building a mixedinitiative esc system: 1) when should the system take the initiative during the conversation? 2) what kind of information is required for the system to initiate a subdialogue? 3) how could the system facilitate the mixed-initiative interactions? according to these challenges, we define the problem of mixed-initiative esc, which includes three sub-tasks: 1) strategy prediction to determine the mixed-initiative strategy in the next turn, 2) knowledge selection to collect the necessary knowledge for the next turn, and 3) response generation to produce emotional support re-sponses with appropriate mixed-initiative strategy and knowledge. to tackle this problem, we propose a novel framework, named knowledge enhanced mixed-initiative model (kemi), to build a mixedinitiative dialogue system for emotional support conversations with external domain-specific knowledge. in detail, kemi first employs a knowledge acquisition module to acquire emotional support knowledge from a large-scale knowledge graph on mental health dialogues. specifically, we expand the user utterance with generated commonsense knowledge as a query graph and then perform subgraph retrieval over the knowledge graph. secondly, a response generation module conducts multi-task learning of strategy prediction and response generation in a sequence-to-sequence manner to generate mixed-initiative responses with external knowledge. the main contributions of this work are summarized as follows: (1) to measure the mixedinitiative interactions in esc systems, we propose an innovative analysis method, including an eafr annotation schema and corresponding emotional support metrics. (2) we propose a novel knowledge-enhanced mixed-initiative framework for esc, which retrieves external knowledge from mental health knowledge graph by subgraph retrieval using the query graph expanded with commonsense knowledge. (3) experimental results show that the mixed initiative is of great importance in esc, and the proposed method effectively outperforms existing methods on both contentpreserving evaluation and mixed initiative analyses. in addition, measuring mixed initiative is also regarded as an essential perspective for assessing dialogue quality @xcite @xcite . due to the high expenses in human evaluation, @xcite and zhang and balog (2020) investigate user simulation for evaluating the mixed-initiative interactions in conversational systems. in this work, we investigate several metrics for measuring the characteristics of the mixed initiative in esc systems. ## method motivated by the analysis in the last section, we propose the kemi framework that aims to generate mixed-initiative responses with external knowledge. as illustrated in figure 2 , kemi contains two parts: 1) knowledge acquisition, and 2) mixed-initiative response generation. ## conclusions in this paper, we design a novel analysis framework for analyzing the feature of mixed initiative in esc. the analysis demonstrates the necessity and importance of mixed-initiative interactions in esc systems. to this end, we propose the kemi framework to tackle the problem of mixed-initiative esc. kemi first retrieves actual case knowledge from a large-scale mental health knowledge graph with query expansion and subgraph retrieval. then kemi performs multi-task learning of strategy prediction and response generation with the retrieved knowledge. extensive experiments show that kemi outperforms existing methods on both automatic and human evaluation. the analysis also shows the effectiveness of incorporating actual case knowledge and the superiority of kemi on the mixed-initiative interactions.