# An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking

arXiv: https://arxiv.org/abs/1805.01555 · HTML: https://mlsanity.com/paper/1805.01555/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

## Built on by (most cited first, 27 of 27)

- [Continual Prompt Tuning for Dialog State Tracking](https://mlsanity.com/paper/2203.06654/) (2022)
- [Zero-shot Generalization in Dialog State Tracking through Generative Question Answering](https://mlsanity.com/paper/2101.08333/) (2021)
- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking](https://mlsanity.com/paper/2205.02550/) (2022)
- [Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking](https://mlsanity.com/paper/2205.10059/) (2022)
- [Multimodal Dialogue State Tracking](https://mlsanity.com/paper/2206.07898/) (2022)
- [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)
- [Diable: Efficient Dialogue State Tracking as Operations on Tables](https://mlsanity.com/paper/2305.17020/) (2023)
- [Task-oriented Dialogue Systems: performance vs. quality-optima, a review](https://mlsanity.com/paper/2112.11176/) (2021)
- [MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking](https://mlsanity.com/paper/2211.05503/) (2022)
- [Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Data](https://mlsanity.com/paper/2307.01764/) (2023)
- [Task Conditioned BERT for Joint Intent Detection and Slot-filling](https://mlsanity.com/paper/2308.06165/) (2023)
- [OLViT: Multi-Modal State Tracking via Attention-Based Embeddings for Video-Grounded Dialog](https://mlsanity.com/paper/2402.13146/) (2024)
- [Multi-Modal Video Dialog State Tracking in the Wild](https://mlsanity.com/paper/2407.02218/) (2024)
- [A Dialogue-based Information Extraction System for Medical Insurance Assessment](https://mlsanity.com/paper/2107.05866/) (2021)
- [Prompt-based Generative Approach towards Multi-Hierarchical Medical Dialogue State Tracking](https://mlsanity.com/paper/2203.09946/) (2022)
- [XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking](https://mlsanity.com/paper/2204.05895/) (2022)
- [Lexicon-injected Semantic Parsing for Task-Oriented Dialog](https://mlsanity.com/paper/2211.14508/) (2022)
- [ShopTalk: A System for Conversational Faceted Search](https://mlsanity.com/paper/2109.00702/) (2021)
- [CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking](https://mlsanity.com/paper/2210.05146/) (2022)
- [Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking](https://mlsanity.com/paper/2302.08220/) (2023)
- [Learn to Focus: Hierarchical Dynamic Copy Network for Dialogue State Tracking](https://mlsanity.com/paper/2107.11778/) (2021)
- [Ontology-Enhanced Slot Filling](https://mlsanity.com/paper/2108.11275/) (2021)
- [Improving Dialogue State Tracking by Joint Slot Modeling](https://mlsanity.com/paper/2109.14144/) (2021)
- [Candidate Attended Dialogue State Tracking Using BERT](https://mlsanity.com/paper/2607.16021/) (2026)

## Abstract (arXiv)

We highlight a practical yet rarely discussed problem in dialogue state tracking (DST), namely handling unknown slot values. Previous approaches generally assume predefined candidate lists and thus are not designed to output unknown values, especially when the spoken language understanding (SLU) module is absent as in many end-to-end (E2E) systems. We describe in this paper an E2E architecture based on the pointer network (PtrNet) that can effectively extract unknown slot values while still obtains state-of-the-art accuracy on the standard DSTC2 benchmark. We also provide extensive empirical evidence to show that tracking unknown values can be challenging and our approach can bring significant improvement with the help of an effective feature dropout technique.