Dataset versus reality: Understanding model performance from the perspective of information need

Abstract

Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real-world problems with similar settings (e.g., identical input/output) to the benchmark datasets? We argue that a model is trained to answer the same information need in a similar context (e.g., the information available), for which the training dataset is created. The trained model may be used to solve real-world problems for a similar information need in a similar context. However, information need is independent of the format of dataset input/output. Although some datasets may share high structural similarities, they may represent different research tasks aiming for answering different information needs. Examples are question–answer pairs for the question answering (QA) task, and image-caption pairs for the image captioning (IC) task. In this paper, we use the QA task and IC task as two case studies and compare their widely used benchmark datasets. From the perspective of information need in the context of information retrieval, we show the differences in the dataset creation processes and the differences in morphosyntactic properties between datasets. The differences in these datasets can be attributed to the different information needs and contexts of the specific research tasks. We encourage all researchers to consider the information need perspective of a research task when selecting the appropriate datasets to train a model. Likewise, while creating a dataset, researchers may also incorporate the information need perspective as a factor to determine the degree to which the dataset accurately reflects the real-world problem or the research task they intend to tackle.