# MIDAS: A Dialog Act Annotation Scheme for Open Domain Human Machine Spoken Conversations

arXiv: https://arxiv.org/abs/1908.10023 · HTML: https://mlsanity.com/paper/1908.10023/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

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## Abstract (arXiv)

Dialog act prediction is an essential language comprehension task for both dialog system building and discourse analysis. Previous dialog act schemes, such as SWBD-DAMSL, are designed for human-human conversations, in which conversation partners have perfect language understanding ability. In this paper, we design a dialog act annotation scheme, MIDAS (Machine Interaction Dialog Act Scheme), targeted on open-domain human-machine conversations. MIDAS is designed to assist machines which have limited ability to understand their human partners. MIDAS has a hierarchical structure and supports multi-label annotations. We collected and annotated a large open-domain human-machine spoken conversation dataset (consists of 24K utterances). To show the applicability of the scheme, we leverage transfer learning methods to train a multi-label dialog act prediction model and reach an F1 score of 0.79.