# Iterative Delexicalization for Improved Spoken Language Understanding

arXiv: https://arxiv.org/abs/1910.07060 · HTML: https://mlsanity.com/paper/1910.07060/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Compositional Generalization in Spoken Language Understanding](https://mlsanity.com/paper/2312.15815/) (2023)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)
- [Covering Cracks in Content Moderation: Delexicalized Distant Supervision for Illicit Drug Jargon Detection](https://mlsanity.com/paper/2503.14926/) (2025)

## Abstract (arXiv)

Recurrent neural network (RNN) based joint intent classification and slot tagging models have achieved tremendous success in recent years for building spoken language understanding and dialog systems. However, these models suffer from poor performance for slots which often encounter large semantic variability in slot values after deployment (e.g. message texts, partial movie/artist names). While greedy delexicalization of slots in the input utterance via substring matching can partly improve performance, it often produces incorrect input. Moreover, such techniques cannot delexicalize slots with out-of-vocabulary slot values not seen at training. In this paper, we propose a novel iterative delexicalization algorithm, which can accurately delexicalize the input, even with out-of-vocabulary slot values. Based on model confidence of the current delexicalized input, our algorithm improves delexicalization in every iteration to converge to the best input having the highest confidence. We show on benchmark and in-house datasets that our algorithm can greatly improve parsing performance for RNN based models, especially for out-of-distribution slot values.