  Ranking has always been one of the top concerns in information retrieval
researches. For decades, the lexical matching signal has dominated the ad-hoc
retrieval process, but solely using this signal in retrieval may cause the
vocabulary mismatch problem. In recent years, with the development of
representation learning techniques, many researchers turn to Dense Retrieval
(DR) models for better ranking performance. Although several existing DR models
have already obtained promising results, their performance improvement heavily
relies on the sampling of training examples. Many effective sampling strategies
are not efficient enough for practical usage, and for most of them, there still
lacks theoretical analysis in how and why performance improvement happens. To
shed light on these research questions, we theoretically investigate different
training strategies for DR models and try to explain why hard negative sampling
performs better than random sampling. Through the analysis, we also find that
there are many potential risks in static hard negative sampling, which is
employed by many existing training methods. Therefore, we propose two training
strategies named a Stable Training Algorithm for dense Retrieval (STAR) and a
query-side training Algorithm for Directly Optimizing Ranking pErformance
(ADORE), respectively. STAR improves the stability of DR training process by
introducing random negatives. ADORE replaces the widely-adopted static hard
negative sampling method with a dynamic one to directly optimize the ranking
performance. Experimental results on two publicly available retrieval benchmark
datasets show that either strategy gains significant improvements over existing
competitive baselines and a combination of them leads to the best performance.
