# End-to-End Slot Alignment and Recognition for Cross-Lingual NLU

arXiv: https://arxiv.org/abs/2004.14353 · HTML: https://mlsanity.com/paper/2004.14353/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 30 of 83)

- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages](https://mlsanity.com/paper/2204.08582/) (2022)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Language Models are Few-shot Multilingual Learners](https://mlsanity.com/paper/2109.07684/) (2021)
- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers](https://mlsanity.com/paper/2404.04925/) (2024)
- [From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2105.07316/) (2021)
- [Multilingual and Cross-Lingual Intent Detection from Spoken Data](https://mlsanity.com/paper/2104.08524/) (2021)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models](https://mlsanity.com/paper/2404.16233/) (2024)
- [LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging](https://mlsanity.com/paper/2209.09900/) (2022)
- [Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling](https://mlsanity.com/paper/2103.07792/) (2021)
- [ConvFiT: Conversational Fine-Tuning of Pretrained Language Models](https://mlsanity.com/paper/2109.10126/) (2021)
- [Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation](https://mlsanity.com/paper/2201.13405/) (2022)
- [MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2212.13492/) (2022)
- [Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyond](https://mlsanity.com/paper/2408.03900/) (2024)
- [Intent Classification and Slot Filling for Privacy Policies](https://mlsanity.com/paper/2101.00123/) (2021)
- [Intent Detection and Slot Filling for Vietnamese](https://mlsanity.com/paper/2104.02021/) (2021)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [pNLP-Mixer: an Efficient all-MLP Architecture for Language](https://mlsanity.com/paper/2202.04350/) (2022)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data](https://mlsanity.com/paper/2109.04319/) (2021)
- [Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks](https://mlsanity.com/paper/2108.08375/) (2021)
- [PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs](https://mlsanity.com/paper/2303.08954/) (2023)
- [Multi3WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems](https://mlsanity.com/paper/2307.14031/) (2023)
- [Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems](https://mlsanity.com/paper/2206.07808/) (2022)
- [MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2212.10455/) (2022)
- [GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding](https://mlsanity.com/paper/2204.08325/) (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)

## Abstract (arXiv)

Natural language understanding (NLU) in the context of goal-oriented dialog systems typically includes intent classification and slot labeling tasks. Existing methods to expand an NLU system to new languages use machine translation with slot label projection from source to the translated utterances, and thus are sensitive to projection errors. In this work, we propose a novel end-to-end model that learns to align and predict target slot labels jointly for cross-lingual transfer. We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus. Results show that our method outperforms a simple label projection method using fast-align on most languages, and achieves competitive performance to the more complex, state-of-the-art projection method with only half of the training time. We release our MultiATIS++ corpus to the community to continue future research on cross-lingual NLU.