Welcome to part 3 of our series on Neural IR. In this screencast, we will dive into the world of neural information retrieval, examining the inputs, outputs, training, and inference processes involved. Whether you’re an NLU enthusiast or simply interested in maximizing the quality of your information retrieval, this article will provide valuable insights.
Contents
The Need for Neural IR
In traditional IR, the popular retrieval model BM25 is often used for term-matching. While effective, neural IR offers a multitude of advantages. By leveraging the advancements made in natural language understanding (NLU), neural IR enhances the quality of search results. The choice between BM25 and neural IR depends on various factors, including budget constraints and the desired balance between efficiency and effectiveness.
Exploring Effectiveness and Efficiency
To quantify the effectiveness and efficiency of different retrieval models, let’s consider the MS MARCO collection. Effectiveness can be measured using the mean reciprocal rank at cut-off 10 (MRR@10), while efficiency can be measured in terms of latency in milliseconds. The graph below illustrates the MRR values and latency possibilities of BM25 retrieval using the Ansereni toolkit.
As we move forward in this series, we will explore various neural IR models and strive to improve the effectiveness metric while keeping an acceptable level of efficiency.
Understanding Neural IR Models
Neural IR models function as black boxes in the context of this screencast. By feeding a query and a document into the model, we receive a single score that estimates the relevance of the query to that document. This process is repeated for each document we want to score, and the results are sorted by decreasing relevance score, generating the Top-K list of results.
Training the Neural Model
Training the neural model for ranking involves a powerful yet straightforward approach: two-way classification, specifically pair-wise classification. During training, each training instance consists of a query, a relevant or positive document, and an irrelevant or negative document. To optimize the neural network, we employ gradient descent using a simple classification loss, such as CrossEntropy Loss with softmax. The goal is to maximize the score of the positive document and minimize the score assigned to the negative document.
Inference and Ranking
Once the neural ranker is trained, conducting the ranking becomes a straightforward task. Given a query, we pass each document through the neural network, obtaining a score. Sorting all the documents by their scores gives us the Top-K list of documents.
Re-Rankers: Enhancing Efficiency
Dealing with collections that contain millions or billions of documents presents a challenge in terms of efficiency. To address this, neural IR models are often used as re-rankers. These models rescore only the Top-K documents obtained by another model, such as BM25. By doing so, they improve the final ranking while significantly reducing the processing time.
The Future of Neural IR
In our next screencast, we will delve deeper into various neural re-rankers and explore their implementation details. We will also discuss the concept of end-to-end retrieval, where the neural model itself conducts the search over the entire collection without the need for a re-ranking pipeline. Stay tuned for more exciting insights!
FAQs
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What is the difference between traditional IR and neural IR?
Traditional IR often relies on models like BM25 for term-matching, while neural IR enhances search results using advancements in NLU. -
How can neural IR models improve search quality?
Neural IR models leverage NLU techniques to better estimate the relevance of queries to documents, resulting in more accurate search results.
Conclusion
Neural IR brings significant improvements to information retrieval by utilizing NLU advancements. By understanding the inputs, outputs, training, and inference processes of neural IR models, we can optimize the quality and efficiency of search results. Stay tuned for the next installment in our series, where we explore various neural re-rankers in detail. Discover more about the world of technology at Techal.
