Determining the role of event arguments is a crucial subtask of event
extraction. Most previous supervised models leverage costly annotations, which
is not practical for open-domain applications. In this work, we propose to use
global constraints with prompting to effectively tackles event argument
classification without any annotation and task-specific training. Specifically,
given an event and its associated passage, the model first creates several new
passages by prefix prompts and cloze prompts, where prefix prompts indicate
event type and trigger span, and cloze prompts connect each candidate role with
the target argument span. Then, a pre-trained language model scores the new
passages, making the initial prediction. Our novel prompt templates can easily
adapt to all events and argument types without manual effort. Next, the model
regularizes the prediction by global constraints exploiting cross-task,
cross-argument, and cross-event relations. Extensive experiments demonstrate
our model's effectiveness: it outperforms the best zero-shot baselines by 12.5%
and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1,
respectively, without given argument spans. We have made our code publicly
available.

本文使用全局约束和提示来有效地处理事件参数分类，而无需任何标注和特定任务的训练，使用新的提示模板，可以轻松适应所有事件和参数类型，并且在多个数据集上实验证明了该方法的有效性。

带提示的全局约束的零样本事件论元分类

Global Constraints with Prompting for Zero-Shot Event Argument Classification

Using the attention map based probing frame-work from (Clark et al., 2019),
we observe that, on the RAMS dataset (Ebner et al., 2020), BERT's attention
heads have modest but well above-chance ability to spot event arguments sans
any training or domain finetuning, vary-ing from a low of 17.77% for Place to a
high of 51.61% for Artifact. Next, we find that linear combinations of these
heads, estimated with approx 11% of available total event argument detection
supervision, can push performance well-higher for some roles - highest two
being Victim (68.29% Accuracy) and Artifact(58.82% Accuracy). Furthermore, we
investigate how well our methods do for cross-sentence event arguments. We
propose a procedure to isolate "best heads" for cross-sentence argument
detection separately of those for intra-sentence arguments. The heads thus
estimated have superior cross-sentence performance compared to their jointly
estimated equivalents, albeit only under the unrealistic assumption that we
already know the argument is present in an-other sentence. Lastly, we seek to
isolate to what extent our numbers stem from lexical frequency based
associations between gold arguments and roles. We propose NONCE, a scheme to
create adversarial test examples by replacing gold arguments with randomly
generated "nonce" words. We find that learnt linear combinations are robust to
NONCE, though individual best heads can be more sensitive.

使用注意力图探针框架，发现 BERT 模型对事件参数有良好的探测能力，并通过线性组合显著提高了某些角色的性能，尤其是在跨句子事件参数上表现更好。同时，通过提出的 NONCE 方法，可以验证单个最佳头部对词汇频率基础上的黄金参数和角色的关联是否具有鲁棒性。