The Art of Prompting: Event Detection based on Type Specific Prompts (2023)

Abstract: We compare various forms of prompts to represent event types and develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot event detection. The experimental results demonstrate that a well-defined and comprehensive event type prompt can significantly improve event detection performance, especially when the annotated data is scarce (few-shot event detection) or not available (zero-shot event detection). By leveraging the semantics of event types, our unified framework shows up to 22.2% F-score gain over the previous state-of-the-art baselines.

## problem formulation here, we first define each setting of the event detection task and then describe the various forms of event type prompts. ## a unified framework for ed we adapt @xcite and design a unified event detection framework (as shown in figure 1 ) which leverages event type specific prompts to detect events under supervised, few-shot, and zeroshot settings. formally, given an input sentence @xmath0, w 2 , . . . , w n }, we take each event type prompt t @xmath1, τ t 2 , . . . , τ t m } as a query of m tokens to extract triggers for event type t. specifically, we first concatenate them into a sequence we use a pre-trained bert encoder @xcite to get contextual representations for the input sentence @xmath2, w 2 , ..., w n } as well as the event type prompt @xmath3, τ t 1 , ..., τ t m } 2 . given a prompt of each event type, we aim to extract corresponding event triggers from the input sentence. to achieve this goal, we need to capture the semantic correlation of each input token to the event type thus we learn a weight distribution over the sequence of contextual representations of the event type prompt, to obtain event type t aware contextual representation with that, the event type aware contextual representation a t i will be concatenated with the original contextual representation w i from the encoder, and classified into a binary label, indicating whether it is a candidate trigger of event type ## experiment setup we perform experiments on three public benchmark datasets, including ace05-e + (automatic content extraction), ere (entity relation event) @xcite ,and maven @xcite . on each dataset, we conduct experiments for sed, fsed, and zsed. for sed, we use the same data split as the previous studies @xcite @xcite @xcite @xcite ) on all the three benchmark datasets. for fsed and zsed on maven, we follow the previous study @xcite and choose 120 event types with the most frequent mentions as the base event types and the rest 45 event types as novel ones. for fsed and zsed on @xcite to meet the above conditions, for ace, we define the event types of 5 main event categories: business, contact, conflict, justice and movement as the base event types, and types of the remaining 3 main categories: life, personnel and transaction as the novel event types. in total, there are 18 qualified base types and 10 qualified novel types (the others do not satisfy the second condition). for ere, we use the exact same 10 novel event types as ace, and the rest 25 types as base event types. detailed data and hyperparameter descriptions are in appendix d and appendix e. 6 results and discussion overall results the experimental results for sed, fsed, and zsed on ace05, ere, and method sed fsed zsed previous sota 68.5 57.0 40.2* (wang et al., 2021b) (chen et al., 2021) (zhang et al., 2021b) (a) event type name 68.8 63.4 58.8 (b) definition 67.1 56.9 52.9 (c) seed triggers 68.7 65.1 59.1 (e) soft prompt 64.5 38.6 -majority voting of (a-e) 68.4 63.4 58.1 (f) apex prompt 68.8 68.4 59.9 ## conclusion we investigate a variety of prompts to represent the semantics of event types, and leverage them with a unified framework for supervised, few-shot and zero-shot event detection. experimental results demonstrate that, a well-defined and comprehensive description of event types can significantly improve the performance of event detection, especially when the annotations are limited (few-shot event detection) or even not available (zero-shot event detection), with up to 22.2% f-score gain over the prior state of the art.