Spoken Document Retrieval Leveraging Bert-Based Modeling and Query Reformulation

Shao Wei Fan-Jiang, Tien Hong Lo, Berlin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Spoken document retrieval (SDR) has long been deemed a fundamental and important step towards efficient organization of, and access to multimedia associated with spoken content. In this paper, we present a novel study of SDR leveraging the Bidirectional Encoder Representations from Transformers (BERT) model for query and document representations (embeddings), as well as for relevance scoring. BERT has produced extremely promising results for various tasks in natural language understanding, but relatively little research on it is devoted to text information retrieval (IR), let alone SDR. We further tackle one of the critical problems facing SDR, viz. a query is often too short to convey a user's information need, via the process of pseudo-relevance feedback (PRF), showing how information cues induced from PRF can be aptly incorporated into BERT for query expansion. In addition, such query reformulation through PRF also works in conjunction with additional augmentation of lexical features and confidence scores into the document embeddings learned from BERT. The merits of our approach are attested through extensive sets of experiments, which compare it with several classic and cutting-edge (deep learning-based) retrieval approaches.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8144-8148
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - 2020 May
Externally publishedYes
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 2020 May 42020 May 8

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period2020/05/042020/05/08

Keywords

  • BERT
  • information retrieval
  • model augmentation
  • pseudo-relevance feedback
  • query reformulation
  • speech recognition
  • Spoken document retrieval

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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