Applying the graph-based modeling technique to real-world, large-scale conversation datasets presents several challenges and limitations. One significant challenge is the variability in conversational contexts and topics. Real-world conversations often encompass a wide range of subjects, cultural references, and informal language, which can complicate the construction of coherent graphs. The model may struggle to generalize across diverse topics, leading to potential inaccuracies in assessing coherence and proficiency.
Another limitation is the complexity of speaker interactions. In natural conversations, speakers may interrupt each other, overlap in speech, or exhibit non-linear dialogue flows, which can disrupt the hierarchical structure of the graph. Capturing these dynamic interactions requires sophisticated modeling techniques that can account for such variability, which may not be fully addressed by the current hierarchical graph approach.
Additionally, the quality and consistency of annotations in large-scale datasets can vary, impacting the training and evaluation of the model. Inconsistent labeling of discourse relations or speaker intents can lead to noise in the training data, ultimately affecting the model's performance.
Lastly, computational efficiency is a concern when scaling the graph-based approach to large datasets. The complexity of graph construction and the need for real-time processing in interactive applications may pose significant challenges in terms of resource allocation and processing time. Addressing these challenges will require ongoing research and development to refine the modeling techniques and ensure their applicability in diverse, real-world scenarios.