  User-generated texts available on the web and social platforms are often long
and semantically challenging, making them difficult to annotate. Obtaining
human annotation becomes increasingly difficult as problem domains become more
specialized. For example, many health NLP problems require domain experts to be
a part of the annotation pipeline. Thus, it is crucial that we develop
low-resource NLP solutions able to work with this set of limited-data problems.
In this study, we employ Abstract Meaning Representation (AMR) graphs as a
means to model low-resource Health NLP tasks sourced from various online health
resources and communities. AMRs are well suited to model online health texts as
they can represent multi-sentence inputs, abstract away from complex
terminology, and model long-distance relationships between co-referring tokens.
AMRs thus improve the ability of pre-trained language models to reason about
high-complexity texts. Our experiments show that we can improve performance on
6 low-resource health NLP tasks by augmenting text embeddings with semantic
graph embeddings. Our approach is task agnostic and easy to merge into any
standard text classification pipeline. We experimentally validate that AMRs are
useful in the modeling of complex texts by analyzing performance through the
lens of two textual complexity measures: the Flesch Kincaid Reading Level and
Syntactic Complexity. Our error analysis shows that AMR-infused language models
perform better on complex texts and generally show less predictive variance in
the presence of changing complexity.
