Intended Target Identification for Anomia Patients with Gradient-based Selective Augmentation (2024)

Abstract: In this study, we investigate the potential of language models (LMs) in aiding patients experiencing anomia, a difficulty identifying the names of items. Identifying the intended target item from patient’s circumlocution involves the two challenges of term failure and error. (1) The terms relevant to identifying the item remain unseen. (2) What makes the challenge unique is inherent perturbed terms by semantic paraphasia, which are not exactly related to the target item, hindering the identification process. To address each, we propose robustifying the model from semantically paraphasic errors and enhancing the model with unseen terms with gradient-based selective augmentation (GradSelect). Specifically, the gradient value controls augmented data quality amid semantic errors, while the gradient variance guides the inclusion of unseen but relevant terms. Due to limited domain-specific datasets, we evaluate the model on the Tip of the Tongue dataset as an intermediary task and then apply our findings to real patient data from AphasiaBank. Our results demonstrate strong performance against baselines, aiding anomia patients by addressing the outlined challenges.

## introduction despite significant advancements in language models (lms), challenges persist in effectively handling the tail data, such as accommodating the needs of unseen language groups and addressing social biases @xcite . this gap underscores the importance of research endeavors focused on refining lms to better serve underrepresented populations, with individuals having language disorders being no exception. anomia or word-retrieval difficulty stands as one of the most prevalent symptoms of people with aphasia (pwa) @xcite . ## pilot study on circumlocution terms this section discusses the existence and effect of each c1: relevant-unseen and c2: seen-spe term in the circumlocution. ## methods our goal is to identify the intended target item from the item corpus given the circumlocution. the flow of gradselect is depicted in alg. 1. leveraging the gradient as a proxy for the spe, gradselect selectively augments the dataset to make the semantics between circumlocution and the target item properly overlap. it is designed to denoise the spe terms in the seen terms (subsec 3.1), and enhance the circumlocution with unseen but relevant terms in our task (subsec 3.2). ## experiments in this section, we first evaluate our strategy for the intermediary task that simulates item recall difficulties. subsequently, we transition to use the utterances from real-world pwa datasets sourced from aphasiabank @xcite , which allows us to validate our findings in a more clinically relevant context. ## conclusion we studied designing lm to aid anomic patients by identifying the target item that they intend to. we identified two primary challenges in this context raised from unseen and spe terms. to tackle these issues, we proposed a gradient-based selective augmentation strategy, which robustifies the model from spe terms and enhances it with unseen terms. experiments show that addressing each challenge contributes to improving performance. we demonstrate that the gradient works as a proxy for spe, showing its effectiveness in controlling the quality of data augmentation. our approach demonstrated consistent improvements in both anomic patients and healthy individuals experiencing tip-of-the-tongue states, thereby broadening the impact of this research not only on anomia but also on general studies of intended target identification.