Master’s Thesis Presentation • Artificial Intelligence • Navigating Identities in Text: Towards an Approach for Dementia Care

Friday, May 10, 2024 10:00 am - 11:00 am EDT (GMT -04:00)

Please note: This master’s thesis presentation will take place in DC 2314.

Jess Gano, Master's candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jesse Hoey

Identity, as a concept, is concerned with the social positioning of the self and the other. It manifests through discourse and interactions and expressed in relation to other perceived identities. For example, can one be or talk as a leader without strictly categorizing those they interact with as subordinates or employees? Research shows that the onset and progression of dementia may undermine the individual's sense of self and identity. This loss of self or identity has not only been found to cause significant decrease in well-being, but also affect caregiver/care-recipient relationships. However, while identity is compromised in some way, it does not necessarily mean it is completely lost. Autobiographical stories, especially those told repeatedly, may serve as means to reveal significant aspects of the storyteller's self and identity.

In this thesis, we explore the task of persona attribute extraction from dialogues as a proxy for identity cues. We define persona attribute as a triplet in the format of (subject, relation, object) e.g., (I, has_hobby, knitting). Employing an information extraction approach, we design a two-stage persona attribute extractor, consisting of a relation predictor and entity extractor. Respectively, we define relation prediction as a multi-label classification task using BERT embeddings and feedforward neural networks, and entity extraction as a template infilling task following the pre-training objective of T5 (Raffel et al., 2020). We employ our methods on a proxy dataset created by combining Persona-Chat and Dialogue-NLI. Factoring ethical considerations and potential risks, directly evaluating our methods on a dementia use-case is not a feasible task. Therefore, we utilize a dataset consisting of interviews with older adults to assess feasibility within a context more closely resembling the dementia use-case.

Exploring the research problem and developing our methodology highlights the following insights: (1) inferring identities from text, especially considering its nuanced representation in discourse, is challenging due to the abstract nature of identity itself and (2) to our knowledge, there is no available dataset that exhibits the distinct speech characteristics inherent in older adults making training and evaluating models tailored to this demographic very challenging. Furthermore, experiments on the older adults dataset show that a transfer learning approach to solving this problem is insufficient due to significant contrast between the datasets from the source and target domains.