Domain Adaptation for Dense Retrieval and Conversational Search Enhancement through Self-Supervision

Core Concepts

Combining query-generation and self-supervision approaches enhances domain adaptation in dense retrieval and conversational search models.

Abstract

Recent studies have shown limitations in the generalization ability of dense retrieval models to target domains compared to interaction-based models. This paper proposes a method that combines query-generation with self-supervision using pseudo-relevance labeling to address this challenge. By utilizing a T5-3B model for pseudo-positive labeling and meticulous hard negatives, the proposed approach enables domain adaptation with real queries and documents from the target dataset. Experiments demonstrate improvements on baseline models when fine-tuned on pseudo-relevance labeled data. The approach is extended to conversational dense retrieval models by incorporating a query-rewriting module. Different negative sampling strategies are explored, with SimANS hard negative sampling consistently outperforming others. The proposed approach achieves state-of-the-art results in both dense retrieval and conversational search tasks.

Stats

BM25+T53B top positives help improve DR models' generalization ability. SimANS hard negative sampling consistently outperforms other strategies. DoDress-T53B models show significant improvements over baselines.

Quotes

"DoDress-T53B (GPL) shows an 8.6% improvement over GPL." "The proposed pseudo-relevance labeling approach helps dense retrieval models generalize to new domains." "SimANS hard negative sampling consistently performs the best on all datasets."

Key Insights Distilled From

Domain Adaptation for Dense Retrieval and Conversational Dense Retrieval through Self-Supervision by Meticulous Pseudo-Relevance Labeling

by Minghan Li,E... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.08970.pdf

Deeper Inquiries

How can the proposed approach be adapted for different types of datasets beyond those mentioned

The proposed approach of using pseudo-relevance labeling for domain adaptation can be adapted for different types of datasets by adjusting the specific strategies and models used in the process. For instance, instead of relying on T5-3B as the re-ranker model, other advanced language models like BERT, RoBERTa, or GPT could be employed based on the nature of the dataset and task at hand. Additionally, different negative sampling techniques can be explored to tailor them to the characteristics of new datasets. The query rewriting module can also be customized or replaced with other methods depending on the requirements of diverse datasets.

What are the potential limitations or drawbacks of relying solely on self-supervised methods for domain adaptation

While self-supervised methods offer a promising avenue for domain adaptation without requiring human annotations, there are potential limitations to consider. One drawback is that solely relying on self-supervision may not capture all nuances and complexities present in real-world data compared to supervised approaches. Self-supervised methods heavily depend on pre-training data quality and quantity which might limit their effectiveness in certain scenarios where labeled data is scarce or when dealing with highly specialized domains. Moreover, self-supervised learning may struggle with capturing subtle semantic relationships between queries and documents that human annotation could provide.

How might advancements in natural language processing impact the effectiveness of pseudo-relevance labeling in the future

Advancements in natural language processing (NLP) are likely to significantly impact the effectiveness of pseudo-relevance labeling in several ways. Firstly, improvements in language model architectures such as transformer-based models have shown enhanced capabilities in understanding context and semantics within text data. This increased contextual understanding can lead to more accurate generation of pseudo-positive labels during domain adaptation tasks. Furthermore, advancements in unsupervised learning techniques within NLP could enable better representation learning from unannotated data sources, potentially improving the quality and relevance of generated pseudo-labels. Additionally, developments in transfer learning methodologies tailored for NLP tasks could enhance how well models generalize across domains using pseudo-relevance labeling approaches by leveraging knowledge learned from diverse datasets during pre-training phases.