  The automation of extracting argument structures faces a pair of challenges
on (1) encoding long-term contexts to facilitate comprehensive understanding,
and (2) improving data efficiency since constructing high-quality argument
structures is time-consuming. In this work, we propose a novel context-aware
Transformer-based argument structure prediction model which, on five different
domains, significantly outperforms models that rely on features or only encode
limited contexts. To tackle the difficulty of data annotation, we examine two
complementary methods: (i) transfer learning to leverage existing annotated
data to boost model performance in a new target domain, and (ii) active
learning to strategically identify a small amount of samples for annotation. We
further propose model-independent sample acquisition strategies, which can be
generalized to diverse domains. With extensive experiments, we show that our
simple-yet-effective acquisition strategies yield competitive results against
three strong comparisons. Combined with transfer learning, substantial F1 score
boost (5-25) can be further achieved during the early iterations of active
learning across domains.
