# Dialogue Act Sequence Labeling using Hierarchical encoder with CRF

arXiv: https://arxiv.org/abs/1709.04250 · HTML: https://mlsanity.com/paper/1709.04250/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 14 of 14)

- [Speaker Turn Modeling for Dialogue Act Classification](https://mlsanity.com/paper/2109.05056/) (2021)
- [Picking Pearl From Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services](https://mlsanity.com/paper/2105.15065/) (2021)
- [A Neural Question Answering System for Basic Questions about Subroutines](https://mlsanity.com/paper/2101.03999/) (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 Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis](https://mlsanity.com/paper/2109.08412/) (2021)
- [Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents](https://mlsanity.com/paper/2102.08147/) (2021)
- [Structured Prediction in NLP -- A survey](https://mlsanity.com/paper/2110.02057/) (2021)
- [Hierarchical Knowledge Distillation for Dialogue Sequence Labeling](https://mlsanity.com/paper/2111.10957/) (2021)
- [A survey of neural models for the automatic analysis of conversation: Towards a better integration of the social sciences](https://mlsanity.com/paper/2203.16891/) (2022)
- [Causal Inference for Chatting Handoff](https://mlsanity.com/paper/2210.02862/) (2022)
- [Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing](https://mlsanity.com/paper/2304.03730/) (2023)
- [Athena 2.0: Discourse and User Modeling in Open Domain Dialogue](https://mlsanity.com/paper/2308.01887/) (2023)
- [Amulet: Putting Complex Multi-Turn Conversations on the Stand with LLM Juries](https://mlsanity.com/paper/2505.20451/) (2025)
- [From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding](https://mlsanity.com/paper/2511.20547/) (2025)

## Abstract (arXiv)

Dialogue Act recognition associate dialogue acts (i.e., semantic labels) to utterances in a conversation. The problem of associating semantic labels to utterances can be treated as a sequence labeling problem. In this work, we build a hierarchical recurrent neural network using bidirectional LSTM as a base unit and the conditional random field (CRF) as the top layer to classify each utterance into its corresponding dialogue act. The hierarchical network learns representations at multiple levels, i.e., word level, utterance level, and conversation level. The conversation level representations are input to the CRF layer, which takes into account not only all previous utterances but also their dialogue acts, thus modeling the dependency among both, labels and utterances, an important consideration of natural dialogue. We validate our approach on two different benchmark data sets, Switchboard and Meeting Recorder Dialogue Act, and show performance improvement over the state-of-the-art methods by 2.2% and 4.1% absolute points, respectively. It is worth noting that the inter-annotator agreement on Switchboard data set is 84%, and our method is able to achieve the accuracy of about 79% despite being trained on the noisy data.