Efficient Argument Structure Extraction with Transfer Learning and Active Learning
This work addresses the problem of automating argument structure extraction for natural language processing applications, offering incremental improvements through transfer and active learning.
The paper tackled the problem of extracting argument structures by addressing challenges in encoding long-term contexts and improving data efficiency, resulting in a model that significantly outperforms baselines across five domains and achieving substantial F1 score boosts of 5-25 with transfer and active learning.
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.