  Generative retrieval (GR) directly predicts the identifiers of relevant
documents (i.e., docids) based on a parametric model. It has achieved solid
performance on many ad-hoc retrieval tasks. So far, these tasks have assumed a
static document collection. In many practical scenarios, however, document
collections are dynamic, where new documents are continuously added to the
corpus. The ability to incrementally index new documents while preserving the
ability to answer queries with both previously and newly indexed relevant
documents is vital to applying GR models. In this paper, we address this
practical continual learning problem for GR. We put forward a novel
Continual-LEarner for generatiVE Retrieval (CLEVER) model and make two major
contributions to continual learning for GR: (i) To encode new documents into
docids with low computational cost, we present Incremental Product
Quantization, which updates a partial quantization codebook according to two
adaptive thresholds; and (ii) To memorize new documents for querying without
forgetting previous knowledge, we propose a memory-augmented learning
mechanism, to form meaningful connections between old and new documents.
Empirical results demonstrate the effectiveness and efficiency of the proposed
model.
