Generative Retrieval for Book search

Abstract: In book search, relevant book information should be returned in response to a query. Books contain complex, multi-faceted information such as metadata, outlines, and main text, where the outline provides hierarchical information between chapters and sections. Generative retrieval (GR) is a new retrieval paradigm that consolidates corpus information into a single model to generate identifiers of documents that are relevant to a given query. How can GR be applied to book search? Directly applying GR to book search is a challenge due to the unique characteristics of book search: The model needs to retain the complex, multi-faceted information of the book, which increases the demand for labeled data. Splitting book information and treating it as a collection of separate segments for learning might result in a loss of hierarchical information. We propose an effective Generative retrieval framework for Book Search (GBS) that features two main components: data augmentation and outline-oriented book encoding. For data augmentation, GBS constructs multiple query-book pairs for training; it constructs multiple book identifiers based on the outline, various forms of book contents, and simulates real book retrieval scenarios with varied pseudo-queries. This includes coverage-promoting book identifier augmentation, allowing the model to learn to index effectively, and diversity-enhanced query augmentation, allowing the model to learn to retrieve effectively. Outline-oriented book encoding improves length extrapolation through bi-level positional encoding and retentive attention mechanisms to maintain context over long sequences. Experiments on a proprietary Baidu dataset demonstrate that GBS outperforms strong baselines, achieving a 9.8\% improvement in terms of MRR@20, over the state-of-the-art RIPOR method...

• The paper presents a novel framework, GBS, that generates document identifiers to simplify complex book search tasks.
• It employs data augmentation and outline-oriented encoding with bi-level positional encoding and retentive attention for robust hierarchical representation.
• Experimental results on a Baidu dataset demonstrate a 9.8% improvement in MRR@20 over state-of-the-art retrieval methods.

Analysis of "Generative Retrieval for Book Search"

The paper "Generative Retrieval for Book Search" investigates leveraging generative retrieval (GR) methodologies to enhance book search systems. It introduces the Generative retrieval framework for Book Search (GBS), addressing the inherent challenges presented by the complex and multi-faceted nature of book information.

The primary focus of this study lies in effectively adapting GR techniques to the unique requirements of book retrieval. Key challenges highlighted include managing the extensive metadata, detailed outlines, and the main text present within books, which introduce complications not commonly encountered in other textual retrieval tasks such as web search or document retrieval. Centrally, the GR paradigm enables the direct generation of document identifiers, streamlining the retrieval process through the consolidation of corpus information into a unified model.

The authors propose a GBS framework featuring two pivotal components: data augmentation and outline-oriented book encoding. For the data augmentation aspect, the model constructs diverse query-book pairs, simulating real-world retrieval scenarios via varied pseudo-queries and promoting coverage through hierarchical book identifier augmentation. Outline-oriented book encoding enhances the model's ability to retain hierarchical information and process long sequences using a bi-level positional encoding and retentive attention mechanisms.

Experimental evaluation is conducted on a proprietary Baidu dataset, demonstrating GBS's performance improvements over existing retrieval methods. Specifically, GBS achieves a notable 9.8% improvement in Mean Reciprocal Rank (MRR@20) over the state-of-the-art RIPOR method, affirming the robustness and adaptability of GBS in handling the intricacies of book retrieval.

Implications for Future Research

The implementation of GBS suggests several implications for the fields of information retrieval and text processing, notably in improving retrieval efficiencies for complex, multi-layered text data. The advancements in generative retrieval methodologies, particularly as applied to extended content such as books, open avenues for further exploration in AI-driven search engines, potentially influencing adjacent areas such as question answering and educational technology.

Future research avenues may explore the scalability of GR techniques to larger corpora beyond books or investigate hybrid models integrating dense and generative methodologies. Additionally, addressing the computational costs associated with the comprehensive learning involved in GBS will be crucial for broader application and adaptability of such frameworks in diverse domains.

Overall, the study makes substantial contributions to generative retrieval methods by extending these paradigms to the domain of book search, paving the way for more efficient and contextually aware retrieval systems in complex text environments.