Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering
This work addresses the issue of inflated gains in QA research by providing a strong baseline for researchers, though it is incremental as it builds on existing alignment and retrieval concepts.
The paper tackles the problem of evaluating complex question answering systems by proposing a simple, unsupervised alignment and information retrieval baseline that outperforms conventional baselines and many supervised neural networks, achieving results like 47% P@1 on an 8th grade Science QA dataset and approaching state-of-the-art supervised systems on three QA datasets.
While increasingly complex approaches to question answering (QA) have been proposed, the true gain of these systems, particularly with respect to their expensive training requirements, can be inflated when they are not compared to adequate baselines. Here we propose an unsupervised, simple, and fast alignment and information retrieval baseline that incorporates two novel contributions: a \textit{one-to-many alignment} between query and document terms and \textit{negative alignment} as a proxy for discriminative information. Our approach not only outperforms all conventional baselines as well as many supervised recurrent neural networks, but also approaches the state of the art for supervised systems on three QA datasets. With only three hyperparameters, we achieve 47\% P@1 on an 8th grade Science QA dataset, 32.9\% P@1 on a Yahoo! answers QA dataset and 64\% MAP on WikiQA. We also achieve 26.56\% and 58.36\% on ARC challenge and easy dataset respectively. In addition to including the additional ARC results in this version of the paper, for the ARC easy set only we also experimented with one additional parameter -- number of justifications retrieved.