@inproceedings{b0cba3ac381d48fbac8c1f5d9cf2d4cf,
title = "FABERT: A Feature Aggregation BERT-Based Model for Document Reranking",
abstract = "In a document reranking task, pre-trained language models such as BERT have been successfully applied due to their powerful capability in extracting informative features from queries and candidate answers. However, these language models always generate discriminative features and pay less attention to generalized features which contain shared information of query-answer pairs to assist question answering. In this paper, we propose a BERT-based model named FABERT by integrating both discriminative features and generalized features produced by a gradient reverse layer into one answer vector with an attention mechanism for document reranking. Extensive experiments on the MS MARCO passage ranking task and TREC Robust dataset show that FABERT outperforms baseline methods including a feature projection method which projects existing feature vectors into the orthogonal space of generalized feature vector to eliminate common information of generalized feature vectors.",
keywords = "BERT, Document ranking, Feature aggregation",
author = "Xiaozhi Zhu and Wong, {Leung Pun} and Lee, {Lap Kei} and Hai Liu and Tianyong Hao",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021 ; Conference date: 13-10-2021 Through 17-10-2021",
year = "2021",
doi = "10.1007/978-3-030-88483-3_11",
language = "English",
isbn = "9783030884826",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "139--150",
editor = "Lu Wang and Yansong Feng and Yu Hong and Ruifang He",
booktitle = "Natural Language Processing and Chinese Computing - 10th CCF International Conference, NLPCC 2021, Proceedings",
}