Beyond Questions: Leveraging ColBERT for Keyphrase Search (2412.03193v1)

Abstract: While question-like queries are gaining popularity and search engines' users increasingly adopt them, keyphrase search has traditionally been the cornerstone of web search. This query type is also prevalent in specialised search tasks such as academic or professional search, where experts rely on keyphrases to articulate their information needs. However, current dense retrieval models often fail with keyphrase-like queries, primarily because they are mostly trained on question-like ones. This paper introduces a novel model that employs the ColBERT architecture to enhance document ranking for keyphrase queries. For that, given the lack of large keyphrase-based retrieval datasets, we first explore how LLMs can convert question-like queries into keyphrase format. Then, using those keyphrases, we train a keyphrase-based ColBERT ranker (ColBERTKP_QD) to improve the performance when working with keyphrase queries. Furthermore, to reduce the training costs associated with training the full ColBERT model, we investigate the feasibility of training only a keyphrase query encoder while keeping the document encoder weights static (ColBERTKP_Q). We assess our proposals' ranking performance using both automatically generated and manually annotated keyphrases. Our results reveal the potential of the late interaction architecture when working under the keyphrase search scenario.

• The paper presents two ColBERT adaptations that enhance keyphrase search by fine-tuning both query and document encoders or just the query encoder.
• It leverages LLM-generated keyphrase datasets to overcome the limitations of models trained on question-like queries.
• Experimental results on benchmarks like TREC demonstrate significant improvements in MAP@1000 and nDCG@10, confirming the models’ versatility and efficiency.

Keyphrase Search with ColBERT: Analysis and Implications

The paper "Beyond Questions: Leveraging ColBERT for Keyphrase Search" presents a nuanced exploration of adapting dense retrieval models, notably ColBERT, beyond their conventional focus on question-like queries to encompass keyphrase-based search scenarios. The authors, Jorge Gabín et al., articulate a comprehensive investigation into the challenges and methodologies for enhancing retrieval performance when queries are formatted as keyphrases, which are prevalent in specialized domains such as academic and professional search settings.

Core Contributions and Methodology

The paper identifies a significant limitation in existing dense retrieval models which are predominantly trained on datasets featuring question-like queries, possibly resulting in suboptimal performance when faced with keyphrase-generic queries. To address this gap, the authors propose a novel approach leveraging the ColBERT architecture. The two significant adaptations they introduce include:

1. ColBERTKP$_{QD}$: A dual-adaptation model involving the training of both the keyphrase query encoder and the document encoder. This approach is aimed at improving model effectiveness by fine-tuning the model on datasets formulated in keyphrase format.
2. ColBERTKP$_{Q}$: A more computationally economical variant focusing on training only the query encoder while keeping the document encoder static, leveraging pre-trained weights. This model capitalizes on the robustness of ColBERT's document representation while optimizing query representation for keyphrase searches.

In the absence of a large keyphrase-centric training corpus, the authors innovate by employing LLMs to convert existing question-like queries into keyphrases. This conversion serves as the bedrock for creating both training and evaluation datasets.

Experimental Insights and Metrics

The authors conduct extensive evaluations including comparisons with existing baselines, both on automatically generated and manually annotated keyphrase queries. Their experiments, which use benchmarks such as TREC 2019 and 2020 Deep Learning tracks, reveal the following insights:

• ColBERTKP$_{QD}$ and ColBERTKP$_{Q}$ both surpass traditional dense retrieval models significantly in keyphrase search settings, with improvements noted in metrics like MAP@1000 and nDCG@10.
• The proposed models demonstrate comparable effectiveness to standard models even when applied to the original query types, highlighting their versatility.
• A critical discovery is the models' better handling of different matching strategies, notably special token matching, which ensures robust performance across varied query formulations.
• The utility of the proposed models is further extended to traditional title-query tasks, where they continue to outperform baseline models.

Implications and Future Directions

The implications of this research are substantial for information retrieval systems operating in specialized domains. By tailoring retrieval models to keyphrase queries, there is a potential to enhance the accuracy and relevancy of search results where precise conceptual matching is more critical than generic search strategies. Moreover, the resource-efficient training approach proposed in ColBERTKP$_{Q}$ provides a viable solution for deploying adaptable retrieval systems without extensive computational overhead.

Looking forward, the paper opens up several avenues for exploration. One such direction is the application of these keyphrase-enhanced models to boolean query processing, which also relies on keyphrase constructs joined by logical operators. Additionally, integrating these models with automatic query classification systems could create dynamic pipelines that optimize retrieval strategies based on query type.

Overall, the authors contribute significantly to advancing retrieval methodologies by bridging gaps between question-focused and keyphrase-centric search frameworks, adapted to the modern era's information retrieval requirements.