Papers by Yusufcan Manav
Dealing with Data Scarcity in Spoken Question Answering (2024.lrec-main)
Copied to clipboard
| Challenge: | erroneous automatic speech recognition transcriptions and data scarcity hinder spoken QA models . paper focuses on using limited annotated data to improve spoken qa performance . |
| Approach: | They propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. |
| Outcome: | The proposed model produces question-answer pairs from unannotated data with 5.5% relative gain over the model trained with annotated datasets. |
A Framework for Automatic Generation of Spoken Question-Answering Data (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing frameworks to automatically generate a spoken question answering dataset are limited by the amount of spoken text documents available. |
| Approach: | They propose to use QG module to generate questions from text documents, TTS module to convert text documents into spoken form and automatic speech recognition module to transcribe spoken content. |
| Outcome: | The proposed framework is efficient for automatically generating spoken QA datasets. |