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Uncertainty-Aware Representations for spoken question answering

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Conference Paper (704.5Kb)

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info:eu-repo/semantics/closedAccess

Date

2021

Author

Arısoy, Ebru
Ünlü, Merve

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Citation

Unlu, M., & Arisoy, E., (January 19, 2021). Uncertainty-Aware Representations for spoken question answering. 2021 IEEE Spoken Language Technology Workshop, SLT 2021; Virtual, Shenzhen; China. p. 943-949.

Abstract

This paper describes a spoken question answering system that utilizes the uncertainty in automatic speech recognition (ASR) to mitigate the effect of ASR errors on question answering. Spoken question answering is typically performed by transcribing spoken con-tent with an ASR system and then applying text-based question answering methods to the ASR transcriptions. Question answering on spoken documents is more challenging than question answering on text documents since ASR transcriptions can be erroneous and this degrades the system performance. In this paper, we propose integrating confusion networks with word confidence scores into an end-to-end neural network-based question answering system that works on ASR transcriptions. Integration is performed by generating uncertainty-aware embedding representations from confusion networks. The proposed approach improves F1 score in a question answering task developed for spoken lectures by providing tighter integration of ASR and question answering.

Source

2021 IEEE Spoken Language Technology Workshop, SLT 2021

URI

https://doi.org/10.1109/SLT48900.2021.9383547
https://hdl.handle.net/20.500.11779/1478

Collections

  • Araştırma Çıktıları, Scopus İndeksli Yayınlar Koleksiyonu [376]
  • Araştırma Çıktıları, WOS İndeksli Yayınlar Koleksiyonu [433]
  • MF, EEM, Bildiri ve Sunum Koleksiyonu [27]



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