KiPT: Knowledge-injected Prompt Tuning for Event Detection

Event detection aims to detect events from the text by identifying and classifying event triggers (the most representative words). Most of the existing works rely heavily on complex downstream networks and require sufficient training data. Thus, those models may be structurally redundant and perform poorly when data is scarce. Prompt-based models are easy to build and are promising for few-shot tasks. However, current prompt-based methods may suffer from low precision because they have not introduced event-related semantic knowledge (e.g., part of speech, semantic correlation, etc.). To address these problems, this paper proposes a Knowledge-injected Prompt Tuning (KiPT) model. Specifically, the event detection task is formulated into a condition generation task. Then, knowledge-injected prompts are constructed using external knowledge bases, and a prompt tuning strategy is leveraged to optimize the prompts.

Event Detection (ED) task is one of the essential tasks in the Information Extraction field. Event triggers are the most representative words or phrases in events, and they are usually composed of verbs or nouns.

Specifically, prompt-based methods first transform the original input into prompt templates containing the initial input, the prompt tokens, and unfilled slots for output. Then, a pre-trained language model is employed to fill the unfilled slots to obtain a final string from which the final output can be derived.