用梯度筛选增强方法，帮失语症患者更准识别想说的词。
Intended Target Identification for Anomia Patients with Gradient-based Selective Augmentation
- 用梯度值控制数据增强质量，应对语义错述干扰。
- 梯度方差引导引入未见但相关词汇，提升模型覆盖性。
- 在真实患者数据上验证有效，适合临床辅助应用。
本研究探索语言模型在帮助失语症患者识别物品名称方面的潜力。患者在描述目标物品时存在术语缺失和错误问题：（1）与目标相关的术语可能未被提及；（2）语义换喻导致的干扰项虽不准确却影响判断。为此，我们提出基于梯度的选择性增强方法，通过梯度值控制增强数据质量，以抵御语义错述；利用梯度方差引导引入未见但相关术语。由于领域专用数据集有限，我们在Tip-of-the-Tongue数据集上进行中介任务评估，并将结果应用于AphasiaBank的真实患者数据。实验表明，该方法优于基线，在解决上述挑战方面表现优异。
原文摘要 · Abstract (English)
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. 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.
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