Adaptability of Neural Networks on Varying Granularity IR Tasks
This addresses the adaptability challenge for IR practitioners using deep learning, but it appears incremental as it focuses on evaluating existing methods rather than introducing new ones.
The paper investigates how the granularity of text data affects the performance of deep neural networks in information retrieval tasks, finding that existing models are often tailored to specific datasets and may not adapt well to varying levels of text complexity.
Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks. Deep neural networks (DNN) are capable of learning ideal representations of data during the training process, removing the need for independently extracting features. However, the structures of these DNNs are often tailored to perform on specific datasets. In addition, IR tasks deal with text at varying levels of granularity from single factoids to documents containing thousands of words. In this paper, we examine the role of the granularity on the performance of common state of the art DNN structures in IR.