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Enhancing Retrieval in Theme-specific Applications with Corpus Topical Taxonomy

Core Concepts
The author proposes the use of a corpus topical taxonomy to improve retrieval in theme-specific applications by identifying central topics and leveraging topical relatedness. The ToTER framework enhances PLM-based retrievers through this approach.
The paper introduces the ToTER framework, which leverages a corpus topical taxonomy to enhance retrieval in specialized applications. It addresses challenges like specialized terminologies, limited query contexts, and specific user intents. By combining semantic matching with topical relatedness, ToTER improves accuracy in theme-specific retrieval scenarios. The study compares various methods for improving retrieval accuracy, including search space adjustment, class relevance learning, and query enrichment by core phrases. Results show that ToTER consistently outperforms other techniques in enhancing retrieval effectiveness.
Document retrieval has greatly benefited from large-scale pre-trained language models (PLMs). Extensive quantitative, ablative, and exploratory experiments were conducted on two real-world datasets. The proposed ToTER framework consistently improves retrieval accuracy in scenarios with no labeled data. The corpus topical taxonomy outlines the latent topic hierarchy within the corpus as a tree structure. TopicGQA leverages topic knowledge extracted using PLMs but shows limited effectiveness compared to other methods.
"To harness this corpus-level knowledge for retrieval, we first link it to individual documents." "Using each document as a query, we retrieve a small subset of semantically similar documents." "The taxonomy reveals the latent structure of the whole corpus."

Deeper Inquiries

How can the ToTER framework be adapted for different domains beyond academic papers and product searches

To adapt the ToTER framework for different domains beyond academic papers and product searches, several adjustments can be made. Firstly, the corpus topical taxonomy used in ToTER can be customized to reflect the specific topics and themes relevant to the new domain. This involves constructing a new taxonomy based on seed knowledge related to that particular field or industry. The taxonomy should capture the unique terminologies, concepts, and relationships within the domain. Secondly, the class relevance learning component of ToTER can be fine-tuned using data from the new domain. By training the class relevance estimator on documents from this domain, it can better identify central topics and improve retrieval accuracy. Additionally, for search space adjustment (SSA), understanding how topic overlap works in different domains is crucial. Adjusting parameters such as filtering thresholds based on empirical observations in each domain will optimize SSA performance. For retrieval and reranking stages, incorporating specialized techniques or models tailored to specific domains may enhance performance. For example, utilizing domain-specific pre-trained language models or incorporating additional features relevant to that particular industry could further boost retrieval effectiveness.

What potential limitations or drawbacks could arise from relying heavily on a corpus topical taxonomy for retrieval

While relying heavily on a corpus topical taxonomy for retrieval offers significant benefits in theme-specific applications like academic papers and product searches, there are potential limitations and drawbacks to consider: Limited Coverage: The topical taxonomy may not cover all possible topics or subtopics within a diverse dataset accurately. This limitation could result in missing out on relevant documents that fall outside of predefined categories. Maintenance Overhead: Keeping the corpus topical taxonomy up-to-date with evolving trends or emerging topics requires continuous effort and resources. Without regular updates, its effectiveness may diminish over time. Overfitting: Depending too heavily on a fixed set of topic classes defined by the taxonomy might lead to overfitting during training if there is insufficient diversity in document representations across those classes. Interpretability Challenges: Understanding how exactly documents are classified into specific topic classes by the model might pose challenges for users trying to interpret retrieval results effectively.

How might advancements in PLMs impact the effectiveness of frameworks like ToTER in the future

Advancements in Pre-trained Language Models (PLMs) have already had a significant impact on information retrieval systems like ToTER: 1- Improved Semantic Understanding: As PLMs continue to evolve with larger datasets and more sophisticated architectures like GPT-4 or future iterations of BERT-based models, they will likely offer even better semantic understanding capabilities essential for tasks like document retrieval. 2- Enhanced Contextual Relevance: Future PLMs may incorporate more contextual information at both query-document level interactions leading to improved matching accuracy between user queries and retrieved documents. 3- Domain Adaptation Capabilities: Advancements in transfer learning techniques could enable PLMs trained specifically for certain industries or fields making them more adaptable across various domains without extensive fine-tuning requirements. In conclusion advancements in PLM technology are expected to significantly enhance frameworks like ToTER by providing richer context understanding which would ultimately lead to more accurate and effective retrieval results in a wide range of domains beyond academic papers and product searches..