DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset
read the original 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.
This paper has not been read by Pith yet.
Forward citations
Cited by 3 Pith papers
-
MUSCAT: MUltilingual, SCientific ConversATion Benchmark
MUSCAT is a benchmark of bilingual scientific conversations designed to evaluate ASR systems on code-switching and domain-specific challenges.
-
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.
-
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.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.