Investigation of Ensemble Models For Sequence Learning

While ensemble models have proven useful for sequence learning tasks there is relatively fewer work that provide insights into what makes them powerful. In this paper, we investigate the empirical behavior of the ensemble approaches on sequence modeling, specifically for the semantic tagging task. We explore this by comparing the performance of commonly used and easy to implement ensemble methods such as majority voting, linear combination and stacking to a learning based and rather complex ensemble method. Next, we ask the question: when models of different learning methods such as predictive and representation learning (e.g., deep learning) are aggregated, do we get performance gains over the individual baseline models. We explore these questions on a range of datasets on syntactic and semantic tagging tasks such as slot filling. Our findings show that a ranking based ensemble model outperforms all other well-known ensemble models.