CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search (2011.01580v1)

Abstract: Neural rankers based on deep pretrained LLMs (LMs) have been shown to improve many information retrieval benchmarks. However, these methods are affected by their the correlation between pretraining domain and target domain and rely on massive fine-tuning relevance labels. Directly applying pretraining methods to specific domains may result in suboptimal search quality because specific domains may have domain adaption problems, such as the COVID domain. This paper presents a search system to alleviate the special domain adaption problem. The system utilizes the domain-adaptive pretraining and few-shot learning technologies to help neural rankers mitigate the domain discrepancy and label scarcity problems. Besides, we also integrate dense retrieval to alleviate traditional sparse retrieval's vocabulary mismatch obstacle. Our system performs the best among the non-manual runs in Round 2 of the TREC-COVID task, which aims to retrieve useful information from scientific literature related to COVID-19. Our code is publicly available at https://github.com/thunlp/OpenMatch.

Overview of "CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search"

The paper entitled "CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search" explores methodologies to enhance the performance of information retrieval systems within specialized domains, exemplified by the COVID-19 literature. The authors address the inherent challenges of transferring neural rankers, which are typically trained on web data, to domain-specific retrieval tasks. Key obstacles identified include domain discrepancy, label scarcity, and vocabulary mismatch.

Methodological Contributions

The proposed system leverages several advanced techniques to tackle these challenges:

1. Domain-Adaptive Pretraining (DAPT): The authors apply DAPT to pretrained LLMs to update their semantic understanding with domain-specific corpora. This technique adapts SciBERT using the CORD-19 dataset, ensuring that new terminologies, such as those related to COVID-19, are effectively encoded.
2. Few-Shot Learning Approaches: To address label scarcity, the system employs Contrast Query Generation (ContrastQG) and ReInfoSelect:
   • ContrastQG generates pseudo queries by leveraging consistent signals from text pairs, improving the set of weakly supervised labels.
   • ReInfoSelect utilizes reinforcement learning to filter and select high-quality training data from synthetically generated labels, guiding the model to better performance in the target domain.
3. Dense Retrieval Integration: To surmount the vocabulary mismatch found in traditional sparse ranking models like BM25, dense retrieval techniques are utilized. These methods map queries and documents into a continuous semantic space, enabling improved retrieval accuracy.

Empirical Evaluation

The system was validated using the TREC-COVID Round 2 task, which seeks to extract valuable information from COVID-19 related scientific literature. The system exhibited superior performance among non-manual submissions, as clarified by the significant improvements in NDCG@10 and precision metrics. Notably, the integration of dense retrieval techniques provided substantial enhancements in retrieval efficiency.

Analysis and Implications

The research highlights the efficacy of domain-adaptive approaches in mitigating generalization gaps, evidenced by substantial improvements in specific domain retrieval tasks. By incorporating domain-specific data during pretraining and utilizing strategic pseudo-label generation and selection processes, the system effectively adjusted to the idiosyncrasies of the COVID-19 domain.

These findings imply broader applications in other specialized areas where annotated data scarcity and rapid domain evolution pose significant hurdles. The approach advocates for a flexible pipeline that allows continuous model adaptation as domain-specific data becomes available.

Future Directions

The paper's findings open several avenues for future research:

• Enhanced Pretraining Strategies: Investigations could further explore adaptive pretraining methodologies, possibly incorporating transfer learning from multiple related domains.
• Advanced Label Selection Techniques: There is potential to refine pseudo-labeling methods, especially by integrating more sophisticated reinforcement learning models to better discern high-fidelity training data.
• Scalability of Dense Retrieval: A thorough analysis of how dense retrieval scales across more diverse collections and varying query complexities could inform optimized system architectures.

In summary, the paper provides a well-founded examination and rigorous validation of methods to bridge web domain-trained models to special domain applications, presenting a viable framework for future domain-specific information retrieval solutions.