NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

Li, Canjia; Sun, Yingfei; He, Ben; Wang, Le; Hui, Kai Lung; Yates, Andrew; Sun, Le; Jin, Xu

doi:10.48550/arxiv.1810.12936

2018

DOI: 10.48550/arxiv.1810.12936

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NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

Canjia Li¹,

Yingfei Sun²,

Ben He³

et al.

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2020

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Cited by 2 publications

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Pretrained Transformers for Text Ranking: BERT and Beyond

Lin

Nogueira

Yates

2020

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The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query for a particular task. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has, without exaggeration, revolutionized the fields of natural language processing (NLP), information retrieval (IR), and beyond. In the context of text ranking, these models produce high quality results across many domains, tasks, and settings.

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Pretrained Transformers for Text Ranking: BERT and Beyond

Lin

Nogueira

Yates

2020

Preprint

Self Cite

View full text Add to dashboard Cite

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Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations

Pan¹,

Chen²,

Qiu³

et al. 2020

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Intelligent personal assistant systems for information-seeking conversations are increasingly popular in real-world applications, especially for e-commerce companies. With the development of research in such conversation systems, the pseudo-relevance feedback (PRF) has demonstrated its effectiveness in incorporating relevance signals from external documents. However, the existing studies are either based on heuristic rules or require heavy manual labeling. In this work, we treat the PRF selection as a learning task and proposed a reinforced learning based method that can be trained in an endto-end manner without any human annotations. More specifically, we proposed a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT based response ranker to rank the PRF-enhanced responses. The performance of the ranker serves as rewards to guide the selector to extract useful PRF terms, and thus boost the task performance. Extensive experiments on both standard benchmark and commercial datasets show the superiority of our reinforced PRF term selector compared with other potential soft or hard selection methods. Both qualitative case studies and quantitative analysis show that our model can not only select meaningful PRF terms to expand response candidates but also achieve the best results compared with all the baseline methods on a variety of evaluation metrics. We have also deployed our method on online production in an e-commerce company, which shows a significant improvement over the existing online ranking system. CCS CONCEPTS• Applied computing → Electronic commerce; • Information systems → Retrieval models and ranking.

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NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

Cited by 2 publications

References 13 publications

Pretrained Transformers for Text Ranking: BERT and Beyond

Pretrained Transformers for Text Ranking: BERT and Beyond

Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations

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