A Deep Look into Neural Ranking Models for Information Retrieval (1903.06902v3)

Abstract: Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions.

Overview of "A Deep Look into Neural Ranking Models for Information Retrieval"

The paper "A Deep Look into Neural Ranking Models for Information Retrieval" offers a comprehensive survey of neural ranking models that have been proposed for addressing the ranking problem within Information Retrieval (IR). This work represents an extensive examination of the various methodologies adopted in neural ranking, encompassing both fundamental principles and empirical evaluations, as well as discussions on potential future advancements in the field. This survey emphasizes the transition from traditional IR models, which rely predominantly on heuristic and probabilistic frameworks, towards modern deep learning approaches that promise to overcome the limitations posed by handcrafted features.

The authors categorize neural ranking models into architectural types and define their underlying assumptions and design principles. These models are primarily distinguished into symmetric versus asymmetric, representation-focused versus interaction-focused, and single-granularity versus multi-granularity architectures, all while considering the dimensions of model learning such as pointwise, pairwise, and listwise ranking objectives. Their selection fundamentally informs how heterogeneity between query and document inputs is addressed, how interactions are captured, and the degree to which different granularity levels are integrated.

Model Architectures

Symmetric vs. Asymmetric Architectures: Symmetric architectures are typically employed for scenarios where the inputs (query and document) are homogenous, such as Community Question Answering (CQA) or Automatic Conversation (AC) tasks. In contrast, asymmetric architectures are deemed more suitable for tasks exhibiting significant variability between the input lengths and forms, such as ad-hoc retrieval and standard question-answering tasks.

Representation-focused vs. Interaction-focused Models: The choice between these focuses hinges on the nature of the IR task. Representation-focused models are geared towards generating abstract representations of inputs before computing relevance scores, effective in tasks requiring high-level semantics. In contrast, interaction-focused models prioritize capturing detailed interactions between the query and the document, beneficial for tasks demanding specific matching signals.

Granularity in Modeling: Models employing a single-granularity approach compute relevance based on uniform textual input structures, whereas multi-granularity architectures leverage multi-level abstractions or different text granularities (such as phrases and sentences) to enhance the modeling of relevance. These multi-granularity approaches suit tasks that require both detailed and high-level feature extraction.

Learning Strategies and Empirical Evaluation

The paper evaluates various neural ranking models using well-known IR datasets, such as Robust04, Gov2, and WikiQA, examining the effectiveness of different architectures and learning strategies. Results indicate that while traditional models set strong baselines, neural models demonstrate improvements, particularly on more extensive datasets. This success is partially attributed to the greater model capacity and improved feature learning capabilities of neural models facilitated by data availability.

Future Directions

The paper identifies several trending topics and emerging directions for future research in neural ranking models:

• Indexing Advances: Moving towards real-time ranking by learning to index with neural models, rather than re-ranking, to improve efficiency and accuracy.
• Integration of External Knowledge: Utilizing structured knowledge (e.g., knowledge graphs) and unstructured information (e.g., pseudo-relevance feedback) to enhance ranking models.
• Visual and Contextual Learning: Employing visualization techniques for understanding webpage layouts and integrating context-aware features to improve personalized retrieval tasks.
• Model Interpretability: Developing techniques to demystify neural models, allowing users to understand and interpret the decision-making process.

The survey serves as a significant resource for contextualizing current methodologies and guides researchers in both exploring unexplored facets and pushing the boundaries of what neural ranking models can accomplish within the IR domain.