  In this paper, we propose an effective yet efficient model PAIE for both
sentence-level and document-level Event Argument Extraction (EAE), which also
generalizes well when there is a lack of training data. On the one hand, PAIE
utilizes prompt tuning for extractive objectives to take the best advantages of
Pre-trained Language Models (PLMs). It introduces two span selectors based on
the prompt to select start/end tokens among input texts for each role. On the
other hand, it captures argument interactions via multi-role prompts and
conducts joint optimization with optimal span assignments via a bipartite
matching loss. Also, with a flexible prompt design, PAIE can extract multiple
arguments with the same role instead of conventional heuristic threshold
tuning. We have conducted extensive experiments on three benchmarks, including
both sentence- and document-level EAE. The results present promising
improvements from PAIE (3.5\% and 2.3\% F1 gains in average on three
benchmarks, for PAIE-base and PAIE-large respectively). Further analysis
demonstrates the efficiency, generalization to few-shot settings, and
effectiveness of different extractive prompt tuning strategies. Our code is
available at https://github.com/mayubo2333/PAIE.
