Navigating Identities in Text: Towards an Approach for Dementia Care
Bibliographic record
Abstract
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. \n \nIn 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 (s, r, o), where the relation r indicates the persona attribute type or relationship between the subject s and object o 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, 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. \n \nExploring 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".