# Multi-Layer Ensembling Techniques for Multilingual Intent Classification

arXiv: https://arxiv.org/abs/1806.07914 · HTML: https://mlsanity.com/paper/1806.07914/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models](https://mlsanity.com/paper/2106.08689/) (2021)
- [IntentGPT: Few-shot Intent Discovery with Large Language Models](https://mlsanity.com/paper/2411.10670/) (2024)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)
- [Intent Classification on Low-Resource Languages with Query Similarity Search](https://mlsanity.com/paper/2505.18241/) (2025)

## Abstract (arXiv)

In this paper we determine how multi-layer ensembling improves performance on multilingual intent classification. We develop a novel multi-layer ensembling approach that ensembles both different model initializations and different model architectures. We also introduce a new banking domain dataset and compare results against the standard ATIS dataset and the Chinese SMP2017 dataset to determine ensembling performance in multilingual and multi-domain contexts. We run ensemble experiments across all three datasets, and conclude that ensembling provides significant performance increases, and that multi-layer ensembling is a no-risk way to improve performance on intent classification. We also find that a diverse ensemble of simple models can reach perform comparable to much more sophisticated state-of-the-art models. Our best F 1 scores on ATIS, Banking, and SMP are 97.54%, 91.79%, and 93.55% respectively, which compare well with the state-of-the-art on ATIS and best submission to the SMP2017 competition. The total ensembling performance increases we achieve are 0.23%, 1.96%, and 4.04% F 1 respectively.