# GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

## TL;DR

GumDrop employs a model stacking ensemble approach for discourse unit segmentation and connective detection, demonstrating adaptability across diverse datasets in the DISRPT 2019 Shared Task.

## Contribution

It introduces a novel ensemble stacking method with three component stacks tailored for sentence splitting, discourse segmentation, and connective detection.

## Key findings

- Effective generalization to datasets of varying sizes.
- Improved performance through heterogeneous ensemble models.
- Flexible architecture adaptable to different dataset characteristics.

## Abstract

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.