Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval (1910.10687v2)

Abstract: Term frequency is a common method for identifying the importance of a term in a query or document. But it is a weak signal, especially when the frequency distribution is flat, such as in long queries or short documents where the text is of sentence/passage-length. This paper proposes a Deep Contextualized Term Weighting framework that learns to map BERT's contextualized text representations to context-aware term weights for sentences and passages. When applied to passages, DeepCT-Index produces term weights that can be stored in an ordinary inverted index for passage retrieval. When applied to query text, DeepCT-Query generates a weighted bag-of-words query. Both types of term weight can be used directly by typical first-stage retrieval algorithms. This is novel because most deep neural network based ranking models have higher computational costs, and thus are restricted to later-stage rankers. Experiments on four datasets demonstrate that DeepCT's deep contextualized text understanding greatly improves the accuracy of first-stage retrieval algorithms.

• The paper introduces DeepCT, a novel framework that leverages BERT’s contextual representations to enhance term importance estimation in retrieval systems.
• It details two variants—DeepCT-Index and DeepCT-Query—that integrate with traditional models like BM25 and query likelihood to improve retrieval performance.
• Empirical results on datasets such as MS MARCO demonstrate accuracy improvements up to 27% and 46% for BM25 and QL models, validating the practical impact of the approach.

Context-Aware Term Importance Estimation for First-Stage Retrieval

The paper "Context-Aware Sentence/Passage Term Importance Estimation for First Stage Retrieval" by Zhuyun Dai and Jamie Callan explores an innovative approach to enhancing information retrieval processes by introducing the Deep Contextualized Term Weighting (DeepCT) framework. This paper addresses a significant limitation of traditional frequency-based term weighting by proposing a solution that leverages BERT's deep contextualized text representations to improve the estimation of term importance in both sentences and passages.

Overview and Methodology

In conventional information retrieval systems, the importance of terms in a document is often quantified using term frequency (tf) and inverse document frequency (idf). While this approach has been broadly effective, it falls short in contexts where frequency distributions are flat, such as in short documents and passages. DeepCT seeks to mitigate these shortcomings by using BERT to provide context-aware representations of terms, thereby allowing the determination of term importance based on the semantic content and context of the entire passage.

The authors propose two instantiations of the DeepCT framework:

1. DeepCT-Index: This variant focuses on enhancing term weights for passages. The proposed model produces term weights that can be stored in a conventional inverted index, facilitating efficient first-stage retrieval with common models like BM25 and query likelihood.
2. DeepCT-Query: This variant is employed to generate weighted bag-of-words queries by estimating the importance of query terms, which is particularly beneficial for long queries that mention many terms.

Both applications of DeepCT benefit from utilising BERT's abilities to capture not only the meaning of terms but also the nuances of their contextual significance within a text.

Empirical Evaluations

DeepCT's efficacy is demonstrated through comprehensive experiments across four datasets. In these evaluations, the framework shows substantial improvements in retrieval accuracy compared to traditional frequency-based methods and other neural approaches. Specifically, for first-stage retrieval, DeepCT achieved up to a 50% improvement in accuracy, offering a promising alternative to computationally expensive neural models, which are typically confined to later-stage ranking due to their complexity.

Key Numerical Results

The paper highlights several numerical outcomes. For instance, on the MS MARCO dataset, the application of DeepCT resulted in a 27% and 46% improvement in retrieval accuracy for BM25 and QL models respectively, compared to traditional methods. This showcases the potential of DeepCT in augmenting existing retrieval systems significantly.

Theoretical and Practical Implications

From a theoretical perspective, the introduction of context-aware term weighting extends the boundaries of how semantic understanding can be harnessed in information retrieval. By adapting BERT's deep representations for term weighting, the research paves the way for integrating LLMs more deeply into search infrastructures traditionally limited by frequency-based approaches.

Practically, the implementation of DeepCT as an indexing method indicates that neural models can be repurposed to optimize efficiency in retrieval tasks without the typical trade-offs in performance. This is particularly crucial in real-world settings where computational resources are limited.

Future Developments

Looking forward, this research opens avenues for further exploration into how different contextualized LLMs can impact term importance estimation. Subsequent studies could experiment with models beyond BERT, like GPT or T5, for potential improvements or adaptations. The exploration into methods for balancing the prioritization of contextually important terms against retrieving useful but less-central information could also provide additional enhancements in retrieval strategy.

In sum, the paper offers a novel perspective on term importance estimation that promises to redefine first-stage retrieval by decreasing dependency on term frequency while increasing reliance on context and meaning. Such an approach not only boosts retrieval accuracy but also widens the scope for applying deep learning techniques in efficient and scalable search engines.