Few-Shot Conversational Dense Retrieval (2105.04166v3)

Abstract: 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 https://github.com/thunlp/ConvDR.

Few-Shot Conversational Dense Retrieval

The paper, "Few-Shot Conversational Dense Retrieval", presents an evaluation of Dense Retrieval (DR) in the context of conversational search. It introduces ConvDR, a system that integrates multi-turn conversational queries into dense embeddings for document retrieval. The focus is on overcoming challenges such as dependency on extensive supervision signals and handling conversational queries with long-tail characteristics.

ConvDR incorporates a teacher-student framework that enhances few-shot learning capabilities. The dense retriever uses contextualized embeddings combined with embedding dot products to perform effective document retrieval. The system employs knowledge distillation, leveraging the ad hoc dense retriever as a teacher. The teacher-student framework provides a mechanism for the student query encoder to capture and mimic teacher embeddings, utilizing oracle reformulated queries. Notably, ConvDR outperforms its sparse word-centric predecessors by effectively matching the retrieval accuracy of oracle query reformulations. This results in enhanced efficiency due to the system’s operational simplicity.

The research was evaluated using TREC CAsT and OR-QuAC benchmarks, which demonstrated ConvDR's proficiency in both fully-supervised and few-shot scenarios. ConvDR was shown to maintain retrieval quality throughout evolving conversations, avoiding confusion seen in other systems due to increased noise from previous conversation rounds.

Highlights and Numerical Results

ConvDR achieved significant improvements in retrieval accuracy, demonstrating 9% and 48% advantage over previous models in the experiments conducted on CAsT. The empirical advantages were noted particularly in capturing the necessary and informative context while succeeding in fully supervised contexts, nearly doubling retrieval accuracy compared to previous state-of-the-art benchmarks in the OR-QuAC dataset.

Implications and Future Directions

The implications of this research are profound for both practical and theoretical developments in the field of AI and information retrieval. Practically, the advancements in few-shot learning capabilities allow dense retrieval models to adapt and perform efficiently in a conversational search, which is heavily reliant on rich context understanding. Theoretically, the findings encourage further exploration into augmenting retrieval models with contextual embedding techniques to improve query understanding in complex dialogues.

The paper's methodology and findings open avenues for future research focusing on optimizing knowledge distillation processes and exploring alternative teacher-student frameworks. As AI conversational agents become more sophisticated and prevalent, leveraging dense retrieval mechanisms like ConvDR can lead to widespread improvements in user experience within dynamic information retrieval systems. The research suggests potential paths where adaptive learning models can overcome dependencies on exhaustive data labels and perform efficiently in a data-sparse environment, offering scalable solutions in AI for conversational search challenges.