BERT-QE: Contextualized Query Expansion for Document Re-ranking (Research Paper Walkthrough)
May 17, 2021
#bert #informationretrieval #nlp Query expansion ( QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. This paper introduces BERT to find relevant chunks from the documents as possible augmentations to the given query. ✔️ Automatic Python code generation from Spreadsheet - Mito -
⏩ Abstract: Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant information when expanding the query. To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion. In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models. Please feel free to share out the content and subscribe to my channel :) ⏩ Subscribe -
⏩ OUTLINE: 0:00 - Background and Abstract 1:35 - Method Overview 2:30 - Step 1 in BERT-QE 4:32 - Step 2 in BERT-QE 6:40 - Step 3 in BERT-QE 8:15 - Entire Pipeline Summary ⏩ Paper Title: BERT-QE: Contextualized Query Expansion for Document Re-ranking ⏩ Paper:
⏩ Author: Zhi Zheng, Kai Hui, Ben He, Xianpei Han, Le Sun, Andrew Yates ⏩ Organisation: University of Chinese Academy of Sciences, Amazon Alexa, Institute of Software, Chinese Academy of Sciences, Max Planck Institute for Informatics ⏩ IMPORTANT LINKS Full Playlist on BERT usecases in NLP:
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Background and Abstract
Step 1 in BERT-QE
Step 2 in BERT-QE
Step 3 in BERT-QE
Entire Pipeline Summary
Category: Research Paper
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