Towards Emotional Support Dialog Systems (2106.01144v1)

Abstract: Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. Following reasonable procedures and using various support skills can help to effectively provide support. However, due to the lack of a well-designed task and corpora of effective emotional support conversations, research on building emotional support into dialog systems remains untouched. In this paper, we define the Emotional Support Conversation (ESC) task and propose an ESC Framework, which is grounded on the Helping Skills Theory. We construct an Emotion Support Conversation dataset (ESConv) with rich annotation (especially support strategy) in a help-seeker and supporter mode. To ensure a corpus of high-quality conversations that provide examples of effective emotional support, we take extensive effort to design training tutorials for supporters and several mechanisms for quality control during data collection. Finally, we evaluate state-of-the-art dialog models with respect to the ability to provide emotional support. Our results show the importance of support strategies in providing effective emotional support and the utility of ESConv in training more emotional support systems.

Towards Emotional Support Dialog Systems: An Overview

The paper "Towards Emotional Support Dialog Systems" addresses a critical yet underexplored area of conversation AI: emotional support dialog systems. The authors propose a novel framework for emotional support conversation (ESC) based on the Helping Skills Theory and contribute substantially to the field by constructing an Emotional Support Conversation dataset (ESConv). This work contextualizes emotional support within dialog systems by detailing strategies capable of effectively reducing emotional distress in users.

Key Contributions

1. Task Definition and Framework: The paper defines the Emotional Support Conversation task aimed at social interaction support rather than professional counseling. It proposes an ESC Framework, adapted from Hill's Helping Skills Theory, which consists of three procedural stages—Exploration, Comforting, and Action—and several underlying support strategies. This structured approach provides conceptual clarity and guides the development of AI systems tailored for emotional support.
2. Dataset Construction: A significant contribution is the development of the ESConv dataset, featuring rich annotations on support strategies. Collecting high-quality data, the authors employ a systematic approach involving training crowdworkers and deploying extensive quality control techniques. Such efforts ensure that the corpus provides valuable training examples for dialog systems.
3. Evaluation of Models: The paper evaluates state-of-the-art dialog models, like BlenderBot and DialoGPT, on their ability to incorporate emotional support strategies. They benchmark several variants of these models—Vanilla, Joint, and Oracle—against each other, highlighting the importance of strategy-constrained response generation in enhancing emotional support capabilities.

Numerical Results and Analysis

The paper's experimental results reveal that integrating support strategies into dialog models (Oracle models) significantly improves the models' performance across various metrics, such as BLEU and ROUGE-L scores. For instance, the Oracle variant, which uses ground-truth strategy information, outperformed the Vanilla models, showcasing a better understanding and empathy in response generation. Furthermore, human interactive evaluations confirmed that models trained with ESConv were more effective in providing emotional support compared to untrained models and those with randomly selected strategies.

Implications and Future Directions

This research has both practical and theoretical implications. Practically, it advances the development of dialog systems that can serve in areas such as mental health support and customer service, where emotional intelligence is crucial. Theoretically, it enriches our understanding of interfacing emotional support techniques with conversational AI, setting a precedent for future explorations into nuanced human-computer interactions.

Future research avenues could delve into dynamic strategy selection and user state modeling within ESC, while also addressing the generalization of such frameworks to different cultural contexts. Additionally, broader ethical considerations must be explored, particularly in defining the boundaries of what AI should achieve in emotional support systems.

In conclusion, this paper lays foundational work for integrating emotional support skills into AI dialog systems, offering a well-designed framework and dataset that could significantly propel future advancements in humane AI technologies.