# Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding

arXiv: https://arxiv.org/abs/1604.00117 · HTML: https://mlsanity.com/paper/1604.00117/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2016.

## Built on by (most cited first, 5 of 5)

- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [UNLEARN Efficient Removal of Knowledge in Large Language Models](https://mlsanity.com/paper/2408.04140/) (2024)
- [Commonsense Reasoning for Conversational AI: A Survey of the State of the Art](https://mlsanity.com/paper/2302.07926/) (2023)
- [Few-shot Learning for Slot Tagging with Attentive Relational Network](https://mlsanity.com/paper/2103.02333/) (2021)
- [An empirical assessment of deep learning approaches to task-oriented dialog management](https://mlsanity.com/paper/2108.03478/) (2021)

## Abstract (arXiv)

The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains. The key to scalability is reducing the amount of training data needed to learn a model for a new task. The proposed multi-task model delivers better performance with less data by leveraging patterns that it learns from the other tasks. The approach supports an open vocabulary, which allows the models to generalize to unseen words, which is particularly important when very little training data is used. A newly collected crowd-sourced data set, covering four different domains, is used to demonstrate the effectiveness of the domain adaptation and open vocabulary techniques.