Log In Sign Up

Few-Shot Conversational Dense Retrieval

by   Shi Yu, et al.

Dense retrieval (DR) has the potential to resolve the query understanding challenge in conversational search by matching in the learned embedding space. However, this adaptation is challenging due to DR models' extra needs for supervision signals and the long-tail nature of conversational search. In this paper, we present a Conversational Dense Retrieval system, ConvDR, that learns contextualized embeddings for multi-turn conversational queries and retrieves documents solely using embedding dot products. In addition, we grant ConvDR few-shot ability using a teacher-student framework, where we employ an ad hoc dense retriever as the teacher, inherit its document encodings, and learn a student query encoder to mimic the teacher embeddings on oracle reformulated queries. Our experiments on TREC CAsT and OR-QuAC demonstrate ConvDR's effectiveness in both few-shot and fully-supervised settings. It outperforms previous systems that operate in the sparse word space, matches the retrieval accuracy of oracle query reformulations, and is also more efficient thanks to its simplicity. Our analyses reveal that the advantages of ConvDR come from its ability to capture informative context while ignoring the unrelated context in previous conversation rounds. This makes ConvDR more effective as conversations evolve while previous systems may get confused by the increased noise from previous turns. Our code is publicly available at


page 1

page 2

page 3

page 4


Few-Shot Generative Conversational Query Rewriting

Conversational query rewriting aims to reformulate a concise conversatio...

Zero-shot Query Contextualization for Conversational Search

Current conversational passage retrieval systems cast conversational sea...

Caching Historical Embeddings in Conversational Search

Rapid response, namely low latency, is fundamental in search application...

COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning

We present a new zero-shot dense retrieval (ZeroDR) method, COCO-DR, to ...

Query Resolution for Conversational Search with Limited Supervision

In this work we focus on multi-turn passage retrieval as a crucial compo...

Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder

Dense retrievers encode texts and map them in an embedding space using p...

CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos

Current dense retrievers are not robust to out-of-domain and outlier que...