  Mental health issues differ widely among individuals, with varied signs and
symptoms. Recently, language-based assessments have shown promise in capturing
this diversity, but they require a substantial sample of words per person for
accuracy. This work introduces the task of Adaptive Language-Based Assessment
ALBA, which involves adaptively ordering questions while also scoring an
individual's latent psychological trait using limited language responses to
previous questions. To this end, we develop adaptive testing methods under two
psychometric measurement theories: Classical Test Theory and Item Response
Theory. We empirically evaluate ordering and scoring strategies, organizing
into two new methods: a semi-supervised item response theory-based method ALIRT
and a supervised Actor-Critic model. While we found both methods to improve
over non-adaptive baselines, We found ALIRT to be the most accurate and
scalable, achieving the highest accuracy with fewer questions (e.g., Pearson r
~ 0.93 after only 3 questions as compared to typically needing at least 7
questions). In general, adaptive language-based assessments of depression and
anxiety were able to utilize a smaller sample of language without compromising
validity or large computational costs.
