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SD-QA: Spoken Dialectal Question Answering for the Real World
2021
Findings of the Association for Computational Linguistics: EMNLP 2021
unpublished
Question answering (QA) systems are now available through numerous commercial applications for a wide variety of domains, serving millions of users that interact with them via speech interfaces. However, current benchmarks in QA research do not account for the errors that speech recognition models might introduce, nor do they consider the language variations (dialects) of the users. To address this gap, we augment an existing QA dataset to construct a multi-dialect, spoken QA benchmark on five
doi:10.18653/v1/2021.findings-emnlp.281
fatcat:fjhzgyc2nbfe5m5hkfuohvvl2m