# A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding

arXiv: https://arxiv.org/abs/1804.08064 · HTML: https://mlsanity.com/paper/1804.08064/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2018.

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

- [Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding](https://mlsanity.com/paper/2109.12451/) (2021)
- [Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding](https://mlsanity.com/paper/2201.01420/) (2022)
- [Learning Personalized Representations using Graph Convolutional Network](https://mlsanity.com/paper/2207.14298/) (2022)
- [Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings](https://mlsanity.com/paper/2305.14299/) (2023)
- [Learning Slice-Aware Representations with Mixture of Attentions](https://mlsanity.com/paper/2106.02363/) (2021)
- [Two-stage Voice Application Recommender System for Unhandled Utterances in Intelligent Personal Assistant](https://mlsanity.com/paper/2110.09877/) (2021)

## Abstract (arXiv)

Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping domains for natural language understanding, and the task of finding the best domain to handle an utterance becomes a challenging problem on a large scale. In this paper, we propose a set of efficient and scalable neural shortlisting-reranking models for large-scale domain classification in IPDAs. The shortlisting stage focuses on efficiently trimming all domains down to a list of k-best candidate domains, and the reranking stage performs a list-wise reranking of the initial k-best domains with additional contextual information. We show the effectiveness of our approach with extensive experiments on 1,500 IPDA domains.