A Typology of Synthetic Datasets for Dialogue Processing in Clinical Contexts

Abstract: Synthetic data sets are used across linguistic domains and NLP tasks, particularly in scenarios where authentic data is limited (or even non-existent). One such domain is that of clinical (healthcare) contexts, where there exist significant and long-standing challenges (e.g., privacy, anonymization, and data governance) which have led to the development of an increasing number of synthetic datasets. One increasingly important category of clinical dataset is that of clinical dialogues which are especially sensitive and difficult to collect, and as such are commonly synthesized. While such synthetic datasets have been shown to be sufficient in some situations, little theory exists to inform how they may be best used and generalized to new applications. In this paper, we provide an overview of how synthetic datasets are created, evaluated and being used for dialogue related tasks in the medical domain. Additionally, we propose a novel typology for use in classifying types and degrees of data synthesis, to facilitate comparison and evaluation.

A Typology of Synthetic Datasets for Dialogue Processing in Clinical Contexts

The paper discusses the increasing relevance and application of synthetic datasets in clinical dialogue processing due to the challenges associated with obtaining authentic data. Privacy concerns, data anonymization, and governance issues make genuine clinical conversation data scarce. Hence, synthetic datasets have emerged as viable alternatives, particularly because clinical dialogues are sensitive and often challenging to collect directly.

The authors propose a novel typology for distinguishing various types and degrees of data synthesis which aids in classification and evaluation of synthetic clinical datasets. This typology is grounded in the observation that synthetic datasets are created through distinct mechanisms — process-driven or data-driven methods — each characterized by unique nuances. The intention here is to provide clarity in understanding how these synthetic datasets are formed and used.

The classification of synthetic methods into three types—perturbation of existing data, manual creation of dialogue, and automatic generation using models—reveals that synthetic data is not inherently homogeneous. Instead, it spans a spectrum of syntheticity. Through this, the paper challenges the conventional binary view that data can be strictly categorized as synthetic or real, underscoring that all datasets inherently possess synthetic properties to varying degrees.

The implications here are significant for computational linguistics and NLP applications, especially in the clinical domain. Understanding the nuances in data synthesis informs better utilization and integration for model training and application in medical settings. Synthetic datasets provide the flexibility needed to bridge gaps in data availability and enable development of applications such as medical documentation automation and clinical chatbots. By extending this typology, researchers can critically evaluate the validity and reliability of synthetic datasets, fostering more robust applications in real-world settings.

Moreover, the paper anticipates future research directions in AI, particularly with the growing use of language models for synthetic data generation. The typology can assist researchers in aligning data synthesis practices with broader objectives, ensuring that synthetic dialogues achieve necessary representativeness and functional adequacy in clinical contexts.

Overall, this position paper serves as both a theoretical framework and a practical guide for the generation and evaluation of synthetic datasets in the clinical domain, encouraging further exploration and refinement of such datasets in advanced NLP applications.