Abstract Identifying discourse structures and coherence relations in a piece of text is a fundamental task in natural language processing. The first step of this process is segmenting sentences into clause-like units called elementary discourse units (EDUs). Traditional solutions to discourse segmentation heavily rely on carefully designed features. In this demonstration, we present SEGBOT, a system to split a given piece of text into sequence of EDUs by using an end-to-end neural segmentation model.1 Our model does not require hand-crafted features or external knowledge except word embeddings, yet it outperforms state-of-the-art solutions to discourse segmentation