Introduces MUSCAT benchmark dataset of bilingual scientific discussions to evaluate multilingual ASR performance on code-switching and mixed inputs beyond standard WER.
InInterspeech 2021: The 22nd Annual Conference of the International Speech Communication Association, pages 2881–2885
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems.
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UNVERDICTED 2representative citing papers
Introduces OU-AGs framework and GOOD online inference method to extract ranked natural-language goal distributions from open-ended dialogue, improving alignment in shopping, robotics, and coding domains.
citing papers explorer
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MUSCAT: MUltilingual, SCientific ConversATion Benchmark
Introduces MUSCAT benchmark dataset of bilingual scientific discussions to evaluate multilingual ASR performance on code-switching and mixed inputs beyond standard WER.
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Flexible Agent Alignment with Goal Inference from Open-Ended Dialog
Introduces OU-AGs framework and GOOD online inference method to extract ranked natural-language goal distributions from open-ended dialogue, improving alignment in shopping, robotics, and coding domains.