# Sequential Dialogue Context Modeling for Spoken Language Understanding

arXiv: https://arxiv.org/abs/1705.03455 · HTML: https://mlsanity.com/paper/1705.03455/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2017.

## Built on by (most cited first, 11 of 11)

- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [Commonsense Reasoning for Conversational AI: A Survey of the State of the Art](https://mlsanity.com/paper/2302.07926/) (2023)
- [A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts](https://mlsanity.com/paper/1907.05346/) (2019)
- [A Result based Portable Framework for Spoken Language Understanding](https://mlsanity.com/paper/2103.06010/) (2021)
- [End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting](https://mlsanity.com/paper/2207.08179/) (2022)
- [Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog History](https://mlsanity.com/paper/2305.00926/) (2023)
- [ARTA: Collection and Classification of Ambiguous Requests and Thoughtful Actions](https://mlsanity.com/paper/2106.07999/) (2021)
- [Capture Salient Historical Information: A Fast and Accurate Non-Autoregressive Model for Multi-turn Spoken Language Understanding](https://mlsanity.com/paper/2206.12209/) (2022)
- [MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU](https://mlsanity.com/paper/2408.08144/) (2024)
- [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)
- [Spoken Dialogue System for Medical Prescription Acquisition on Smartphone: Development, Corpus and Evaluation](https://mlsanity.com/paper/2311.03510/) (2023)

## Abstract (arXiv)

Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.