# ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents

arXiv: https://arxiv.org/abs/1806.04327 · HTML: https://mlsanity.com/paper/1806.04327/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 12 of 12)

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- [Dialog Acts for Task-Driven Embodied Agents](https://mlsanity.com/paper/2209.12953/) (2022)
- [Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis](https://mlsanity.com/paper/2309.15656/) (2023)
- [The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs](https://mlsanity.com/paper/2506.12266/) (2025)
- [Zero-Shot Controlled Generation with Encoder-Decoder Transformers](https://mlsanity.com/paper/2106.06411/) (2021)
- [A Systematic Evaluation of Response Selection for Open Domain Dialogue](https://mlsanity.com/paper/2208.04379/) (2022)
- [Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking](https://mlsanity.com/paper/2307.14440/) (2023)
- [Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues](https://mlsanity.com/paper/2106.03065/) (2021)
- [Towards Enhancing Health Coaching Dialogue in Low-Resource Settings](https://mlsanity.com/paper/2404.08888/) (2024)
- [Athena 2.0: Discourse and User Modeling in Open Domain Dialogue](https://mlsanity.com/paper/2308.01887/) (2023)
- [Policy-driven Knowledge Selection and Response Generation for Document-grounded Dialogue](https://mlsanity.com/paper/2410.15970/) (2024)
- [Do You Understand How I Feel?: Towards Verified Empathy in Therapy Chatbots](https://mlsanity.com/paper/2601.08477/) (2026)

## Abstract (arXiv)

Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers' intents, not bound to a particular dialogue system. Unfortunately, publicly available data sets with DA annotation are all based on different annotation schemes and thus incompatible with each other. Moreover, their schemes often do not cover all aspects necessary for open-domain human-machine interaction. In this paper, we propose a methodology to map several publicly available corpora to a subset of the ISO standard, in order to create a large task-independent training corpus for DA classification. We show the feasibility of using this corpus to train a domain-independent DA tagger testing it on out-of-domain conversational data, and argue the importance of training on multiple corpora to achieve robustness across different DA categories.