# Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking

arXiv: https://arxiv.org/abs/2205.10059 · HTML: https://mlsanity.com/paper/2205.10059/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 36 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 29 of 29)

- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines](https://mlsanity.com/paper/2007.12720/) (2020)
- [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://mlsanity.com/paper/1606.03777/) (2016)
- [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://mlsanity.com/paper/2005.02877/) (2020)
- [Dialogue State Tracking with a Language Model using Schema-Driven Prompting](https://mlsanity.com/paper/2109.07506/) (2021)
- [Efficient Dialogue State Tracking by Selectively Overwriting Memory](https://mlsanity.com/paper/1911.03906/) (2019)
- [Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking](https://mlsanity.com/paper/1910.03544/) (2019)
- [Building a Conversational Agent Overnight with Dialogue Self-Play](https://mlsanity.com/paper/1801.04871/) (2018)
- [Dynamically Fused Graph Network for Multi-hop Reasoning](https://mlsanity.com/paper/1905.06933/) (2019)
- [Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents](https://mlsanity.com/paper/1911.00484/) (2019)
- [Question Answering by Reasoning Across Documents with Graph Convolutional Networks](https://mlsanity.com/paper/1808.09920/) (2018)
- [Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs](https://mlsanity.com/paper/1905.07374/) (2019)
- [A Sequence-to-Sequence Approach to Dialogue State Tracking](https://mlsanity.com/paper/2011.09553/) (2020)
- [From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap](https://mlsanity.com/paper/2004.05827/) (2020)
- [BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1907.03040/) (2019)
- [An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking](https://mlsanity.com/paper/1805.01555/) (2018)
- [Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2004.03386/) (2020)
- [A Simple Yet Strong Pipeline for HotpotQA](https://mlsanity.com/paper/2004.06753/) (2020)
- [Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation](https://mlsanity.com/paper/1909.00754/) (2019)
- [A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking](https://mlsanity.com/paper/2006.01554/) (2020)
- [Effective Sequence-to-Sequence Dialogue State Tracking](https://mlsanity.com/paper/2108.13990/) (2021)
- [Multi-task learning for Joint Language Understanding and Dialogue State Tracking](https://mlsanity.com/paper/1811.05408/) (2018)
- [HyST: A Hybrid Approach for Flexible and Accurate Dialogue State Tracking](https://mlsanity.com/paper/1907.00883/) (2019)
- [Graph-free Multi-hop Reading Comprehension: A Select-to-Guide Strategy](https://mlsanity.com/paper/2107.11823/) (2021)
- [Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?](https://mlsanity.com/paper/2105.03571/) (2021)
- [Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking](https://mlsanity.com/paper/2107.12578/) (2021)

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

- [Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2311.06345/) (2023)
- [Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking](https://mlsanity.com/paper/2302.08220/) (2023)
- [Context-Aware Multi-Turn Visual-Textual Reasoning in LVLMs via Dynamic Memory and Adaptive Visual Guidance](https://mlsanity.com/paper/2509.05669/) (2025)
- [From Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors](https://mlsanity.com/paper/2604.23277/) (2026)
- [Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch](https://mlsanity.com/paper/2410.18430/) (2024)
- [From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding](https://mlsanity.com/paper/2511.20547/) (2025)

## Abstract (arXiv)

In dialogue state tracking, dialogue history is a crucial material, and its utilization varies between different models. However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated. Apparently, it requires different dialogue history to update different slots in different turns. Therefore, using consistent dialogue contents may lead to insufficient or redundant information for different slots, which affects the overall performance. To address this problem, we devise DiCoS-DST to dynamically select the relevant dialogue contents corresponding to each slot for state updating. Specifically, it first retrieves turn-level utterances of dialogue history and evaluates their relevance to the slot from a combination of three perspectives: (1) its explicit connection to the slot name; (2) its relevance to the current turn dialogue; (3) Implicit Mention Oriented Reasoning. Then these perspectives are combined to yield a decision, and only the selected dialogue contents are fed into State Generator, which explicitly minimizes the distracting information passed to the downstream state prediction. Experimental results show that our approach achieves new state-of-the-art performance on MultiWOZ 2.1 and MultiWOZ 2.2, and achieves superior performance on multiple mainstream benchmark datasets (including Sim-M, Sim-R, and DSTC2).