Streamlining event relation extraction: A pipeline leveraging pretrained and large language models for inference

Event relation extraction is crucial for understanding the temporal sequence and interconnections between events. To demonstrate this, we developed a Streamlit-based application that showcases our event relation extraction system, capable of identifying semantically accurate relations such as Direct-cause, Enable, Intend, and Prevent. The

system features an API that simplifies inference and displays results in a user-friendly manner. Users can input text like a sentence and the application highlights extracted events and their corresponding relationships. The backend runs a series of pre-trained language models, trained on datasets focused on events and their semantic relations.

The app allows users to switch between various models, including HuggingFace’s RoBERTa, REBEL, and large language models like Zephyr. The demo is available at https://demo.kflow.eurecom.fr/.