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。

简单实体中心问题挑战密集检索器

Simple Entity-Centric Questions Challenge Dense Retrievers

We introduce DELFT, a factoid question answering system which combines the
nuance and depth of knowledge graph question answering approaches with the
broader coverage of free-text. DELFT builds a free-text knowledge graph from
Wikipedia, with entities as nodes and sentences in which entities co-occur as
edges. For each question, DELFT finds the subgraph linking question entity
nodes to candidates using text sentences as edges, creating a dense and high
coverage semantic graph. A novel graph neural network reasons over the
free-text graph-combining evidence on the nodes via information along edge
sentences-to select a final answer. Experiments on three question answering
datasets show DELFT can answer entity-rich questions better than machine
reading based models, bert-based answer ranking and memory networks. DELFT's
advantage comes from both the high coverage of its free-text knowledge
graph-more than double that of dbpedia relations-and the novel graph neural
network which reasons on the rich but noisy free-text evidence.

DELFT 是一个事实性问题回答系统，它将知识图谱问题回答方法的细微差别和深度与自由文本的广泛涵盖相结合。DELFT 从维基百科构建了一个自由文本知识图谱，使用实体作为节点，实体共现的句子作为边。DELFT 针对每个问题在自由文本图中找到将问题实体节点连接到候选节点的子图，使用文本句子作为边，创建稠密和高覆盖率的语义图。通过使用图神经网络，DELFT 可以从自由文本图中推理，通过沿着边句子的信息结合节点上的证据来选择最终答案。