# Multilingual and Cross-Lingual Intent Detection from Spoken Data

arXiv: https://arxiv.org/abs/2104.08524 · HTML: https://mlsanity.com/paper/2104.08524/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://mlsanity.com/paper/2005.00333/) (2020)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks](https://mlsanity.com/paper/1811.01088/) (2018)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [Show Your Work: Improved Reporting of Experimental Results](https://mlsanity.com/paper/1909.03004/) (2019)
- [Benchmarking Natural Language Understanding Services for building Conversational Agents](https://mlsanity.com/paper/1903.05566/) (2019)
- [Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog](https://mlsanity.com/paper/1810.13327/) (2018)
- [Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters](https://mlsanity.com/paper/2007.03001/) (2020)
- [ConveRT: Efficient and Accurate Conversational Representations from Transformers](https://mlsanity.com/paper/1911.03688/) (2019)
- [End-to-End Slot Alignment and Recognition for Cross-Lingual NLU](https://mlsanity.com/paper/2004.14353/) (2020)
- [Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing](https://mlsanity.com/paper/1807.00914/) (2018)
- [Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax](https://mlsanity.com/paper/1902.08564/) (2019)
- [Training Neural Response Selection for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1906.01543/) (2019)
- [Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model](https://mlsanity.com/paper/1810.12836/) (2018)
- [Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval](https://mlsanity.com/paper/2101.08370/) (2021)
- [Almawave-SLU: A new dataset for SLU in Italian](https://mlsanity.com/paper/1907.07526/) (2019)

## Built on by (most cited first, 30 of 35)

- [mSLAM: Massively multilingual joint pre-training for speech and text](https://mlsanity.com/paper/2202.01374/) (2022)
- [A Survey of Large Language Models in Finance (FinLLMs)](https://mlsanity.com/paper/2402.02315/) (2024)
- [DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning](https://mlsanity.com/paper/2310.15205/) (2023)
- [ConvFiT: Conversational Fine-Tuning of Pretrained Language Models](https://mlsanity.com/paper/2109.10126/) (2021)
- [XTREME-S: Evaluating Cross-lingual Speech Representations](https://mlsanity.com/paper/2203.10752/) (2022)
- [Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding](https://mlsanity.com/paper/2305.13512/) (2023)
- [FinMTEB: Finance Massive Text Embedding Benchmark](https://mlsanity.com/paper/2502.10990/) (2025)
- [MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2212.10455/) (2022)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [Extrinsic Evaluation of Machine Translation Metrics](https://mlsanity.com/paper/2212.10297/) (2022)
- [Do We Need Domain-Specific Embedding Models? An Empirical Investigation](https://mlsanity.com/paper/2409.18511/) (2024)
- [NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2204.13021/) (2022)
- [SpeechTaxi: On Multilingual Semantic Speech Classification](https://mlsanity.com/paper/2409.06372/) (2024)
- [FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios](https://mlsanity.com/paper/2507.10448/) (2025)
- [The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation](https://mlsanity.com/paper/2305.09652/) (2023)
- [Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding](https://mlsanity.com/paper/2501.06117/) (2025)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [Breaking Audio Large Language Models by Attacking Only the Encoder: A Universal Targeted Latent-Space Audio Attack](https://mlsanity.com/paper/2512.23881/) (2025)
- [Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Data](https://mlsanity.com/paper/2307.01764/) (2023)
- [SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU](https://mlsanity.com/paper/2311.09502/) (2023)
- [Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study](https://mlsanity.com/paper/2501.03863/) (2025)
- [TCAndon-Router: Adaptive Reasoning Router for Multi-Agent Collaboration](https://mlsanity.com/paper/2601.04544/) (2026)
- [Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss](https://mlsanity.com/paper/2203.09278/) (2022)
- [Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects](https://mlsanity.com/paper/2510.07890/) (2025)
- [A Major Obstacle for NLP Research: Let's Talk about Time Allocation!](https://mlsanity.com/paper/2211.16858/) (2022)
- [Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language Understanding](https://mlsanity.com/paper/2305.12301/) (2023)
- [DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers](https://mlsanity.com/paper/2405.16482/) (2024)
- [AutoIntent: AutoML for Text Classification](https://mlsanity.com/paper/2509.21138/) (2025)
- [OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models](https://mlsanity.com/paper/2603.23938/) (2026)
- [IntentGrasp: A Comprehensive Benchmark for Intent Understanding](https://mlsanity.com/paper/2605.06832/) (2026)

## Abstract (arXiv)

We present a systematic study on multilingual and cross-lingual intent detection from spoken data. The study leverages a new resource put forth in this work, termed MInDS-14, a first training and evaluation resource for the intent detection task with spoken data. It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties. Our key results indicate that combining machine translation models with state-of-the-art multilingual sentence encoders (e.g., LaBSE) can yield strong intent detectors in the majority of target languages covered in MInDS-14, and offer comparative analyses across different axes: e.g., zero-shot versus few-shot learning, translation direction, and impact of speech recognition. We see this work as an important step towards more inclusive development and evaluation of multilingual intent detectors from spoken data, in a much wider spectrum of languages compared to prior work.