# Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning

arXiv: https://arxiv.org/abs/1710.00164 · HTML: https://mlsanity.com/paper/1710.00164/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2017.

## Built on by (most cited first, 6 of 6)

- [A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning](https://mlsanity.com/paper/2202.13675/) (2022)
- [Speaker Turn Modeling for Dialogue Act Classification](https://mlsanity.com/paper/2109.05056/) (2021)
- [Exploring Speaker-Related Information in Spoken Language Understanding for Better Speaker Diarization](https://mlsanity.com/paper/2305.12927/) (2023)
- [A Speaker-aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-turn Dialogue Generation](https://mlsanity.com/paper/2110.06823/) (2021)
- [SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization](https://mlsanity.com/paper/2401.17597/) (2024)
- [Group Preference Collapse in Personalized Multimodal Large Language Models](https://mlsanity.com/paper/2607.22603/) (2026)

## Abstract (arXiv)

Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a role-based contextual model to consider different speaker roles independently based on the various speaking patterns in the multi-turn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns role-specific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasks.