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Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models
Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed
skepticism that neural ranking models were actually improving ad hoc retrieval
effectiveness in limited data scenarios. He provided anecdotal evidence that
authors of neural IR papers demonstrate "wins" by comparing against weak
baselines. This paper provides a rigorous evaluation of those claims in two
ways: First, we conducted a meta-analysis of papers that have reported
experimental results on the TREC Robust04 test collection. We do not find
evidence of an upward trend in effectiveness over time. In fact, the best
reported results are from a decade ago and no recent neural approach comes
close. Second, we applied five recent neural models to rerank the strong
baselines that Lin used to make his arguments. A significant improvement was
observed for one of the models, demonstrating additivity in gains. While there
appears to be merit to neural IR approaches, at least some of the gains
reported in the literature appear illusory.Comment: Published in the Proceedings of the 42nd Annual International ACM
SIGIR Conference on Research and Development in Information Retrieval (SIGIR
2019
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