# Neural-based Context Representation Learning for Dialog Act Classification

arXiv: https://arxiv.org/abs/1708.02561 · HTML: https://mlsanity.com/paper/1708.02561/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2017.

## Built on by (most cited first, 4 of 4)

- [ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet](https://mlsanity.com/paper/2111.14706/) (2021)
- [What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition](https://mlsanity.com/paper/2107.02294/) (2021)
- [A Universality-Individuality Integration Model for Dialog Act Classification](https://mlsanity.com/paper/2204.06185/) (2022)
- [SUMBot: Summarizing Context in Open-Domain Dialogue Systems](https://mlsanity.com/paper/2210.06496/) (2022)

## Abstract (arXiv)

We explore context representation learning methods in neural-based models for dialog act classification. We propose and compare extensively different methods which combine recurrent neural network architectures and attention mechanisms (AMs) at different context levels. Our experimental results on two benchmark datasets show consistent improvements compared to the models without contextual information and reveal that the most suitable AM in the architecture depends on the nature of the dataset.