Open-domain question answering has exploded in popularity recently due to the
success of dense retrieval models, which have surpassed sparse models using
only a few supervised training examples. However, in this paper, we demonstrate
current dense models are not yet the holy grail of retrieval. We first
construct EntityQuestions, a set of simple, entity-rich questions based on
facts from Wikidata (e.g., "Where was Arve Furset born?"), and observe that
dense retrievers drastically underperform sparse methods. We investigate this
issue and uncover that dense retrievers can only generalize to common entities
unless the question pattern is explicitly observed during training. We discuss
two simple solutions towards addressing this critical problem. First, we
demonstrate that data augmentation is unable to fix the generalization problem.
Second, we argue a more robust passage encoder helps facilitate better question
adaptation using specialized question encoders. We hope our work can shed light
on the challenges in creating a robust, universal dense retriever that works
well across different input distributions.

本文主要研究使用 Wikidata 数据构造的 entity-rich questions 在密集检索模型中表现不佳的问题，并探讨了两种解决方案：第一种解决方法是数据扩充无法解决广义化问题，第二种解决方法是更加强大的 passage encoder 有助于使用专门化的问题编码器来更好地适应问题。