Efficient Query Estimation by Vector Averaging in Dual-Encoder Re-Ranking

Abstract

A central problem in information retrieval (IR) is passage ranking, where the task is to retrieve passages from a corpus and order them in decreasing relevance to an arbitrary search query.
Traditional lexical retrieval methods are susceptible to the vocabulary mismatch problem, where relevant passages are overlooked if they do not contain the exact query terms (e.g., synonyms), despite being semantically relevant.
A recent trend in IR is to address this issue by utilizing neural network models (dense rankers) which embed text sequences into dense vector representations that effectively capture their semantics through complex attention mechanisms.
For efficiency, dense rankers are often employed in a retrieve-and-re-rank setting, where a lexical ranker initially retrieves a subset of candidate passages, which are then reordered more accurately by a dense ranker.

In this thesis, we focus on the task of passage re-ranking.
We employ a dual-encoder architecture as re-ranker that employs a two independent query and document encoders, allowing document embeddings to be pre-computed. Dense query-passage similarity is computed as a dot product between their representations.
We then combine scores from both stages using score interpolation.

We identify query encoding latency as a bottleneck and propose an Average Embedding (AvgEmb) estimator. This novel model can efficiently predict an accurate query representation, without requiring any attention-based encoding.
It operates solely on looking up embeddings and computing their weighted average representation.
Our model is distilled from a TCT-ColBERT and achieves 98.6% of its teacher's accuracy while being 13.4X more efficient in query latency and 1.6X better in the full interpolated passage re-ranking pipeline on CPU.

Our code is publicly available on https://github.com/BovdBerg/fast-forward-indexes.