Are Interaction Patterns Helpful for Task-Agnostic Dementia Detection? An Empirical Exploration (2022)

Abstract: Dementia often manifests in dialog through specific behaviors such as requesting clarification, communicating repetitive ideas, and stalling, prompting conversational partners to probe or otherwise attempt to elicit information. Dialog act (DA) sequences can have predictive power for dementia detection through their potential to capture these meaningful interaction patterns. However, most existing work in this space relies on content-dependent features, raising questions about their generalizability beyond small reference sets or across different cognitive tasks. In this paper, we adapt an existing DA annotation scheme for two different cognitive tasks present in a popular dementia detection dataset. We show that a DA tagging model leveraging neural sentence embeddings and other information from previous utterances and speaker tags achieves strong performance for both tasks. We also propose content-free interaction features and show that they yield high utility in distinguishing dementia and control subjects across different tasks. Our study provides a step toward better understanding how interaction patterns in spontaneous dialog affect cognitive modeling across different tasks, which carries implications for the design of non-invasive and low-cost cognitive health monitoring tools for use at scale.

## introduction a recent surge of interest in automated assessment of cognitive health within the speech and language processing communities @xcite @xcite @xcite has spurred the development of high-performing diagnostic models. these models carry the potential for substantial real-world positive impact, offering an affordable and accessible healthcare screening solution for individuals who may otherwise be under-served @xcite . however, recent cognitive assessment models have generally been constrained to specific tasks, each with their own characteristics and requirements. although this facilitates the development of models that excel at their target task (e.g., predicting which users have alzheimer's disease in a picture description task @xcite ), it creates challenges in building generalizable knowledge about the complex relationship between linguistic or verbal behavior and cognitive status. it can be unclear which findings are task-specific, and which may be applicable to different tasks in related settings. in this work, we set out to provide clarity regarding the generalizability of a category of features that have held promise for task-specific cognitive assessment. specifically, we examine facets of individuals' interaction patterns, which have been recognized as predictive of alzheimer's disease or related dementia (ad) in sociolinguistic studies @xcite @xcite and proved informative for automatically detecting ad in task-specific settings @xcite . we do so by adapting an existing dialog act (da) annotation scheme @xcite , previously used to analyze dialogs from ad and control participants, to two distinct cognitive tasks and study subjects' interactions across tasks. we also examine the use of interaction features derived from these da tags in dementia detection models to assess their task-agnostic utility in this domain. our key contributions are as follows: we describe these contributions further in the remainder of this paper. in §2, we review relevant background to position our work within the broader research landscape. in §3, we present our methods for modeling da sequences using the selected da scheme ( §3.1) and developing task-agnostic interaction features within this domain ( §3.2). we describe our data in §4, our experiments in §5, and our results in §6, before concluding in §7. ## experiment we conducted two core sets of experiments in this work. in the first set ( §5.1), we evaluated the performance of our da tagging model (described in §3.1) at correctly assigning labels from our annotation scheme to utterances in the picture description and verbal fluency tasks. in the second ( §5.2), we measured the performance of features designed to capture meaningful interaction patterns using these das when leveraged in a dementia detection task. ## conclusion in this paper we studied the extent to which automated analyses of interaction patterns can be leveraged for task-agnostic dementia detection. to do so, we adapted a da annotation scheme for two different cognitive tasks. we then presented a contextaware da tagging model that uses transfer learning from pre-trained sentence embeddings to compute rich representations of utterances, paired with linguistic features (pos sequences) and speaker tags. the model achieved scores of f 1 =0.75, f 1 =0.85, and f 1 =0.82 in a verbal fluency, picture description, and joint task, respectively. we find that although performance is low for some rare-class das, adding context information and speaker tags boosts performance in several cases. to test the utility of interaction patterns as content-free features, we generate features based on these da tags and other interaction characteristics. we use these to train a random forest classifier for task-agnostic ad detection and achieve strong performance on a joint dataset of picture description and verbal fluency dialogs. these interpretable interaction features in cognitive health screening tasks show promising performance in ad detection. in the future, we will extend this work to create a more balanced (across tasks and ad vs. non-ad classes) cognitive screening dataset, to further test the boundaries to which these results may generalize. these findings will allow us to outline a guiding principal for designing dialog agents for virtual interviewing in the cognitive health screening domain.