# CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing

arXiv: https://arxiv.org/abs/2210.07074 · HTML: https://mlsanity.com/paper/2210.07074/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 49 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Generating Datasets with Pretrained Language Models](https://mlsanity.com/paper/2104.07540/) (2021)
- [Constrained Language Models Yield Few-Shot Semantic Parsers](https://mlsanity.com/paper/2104.08768/) (2021)
- [Good-Enough Compositional Data Augmentation](https://mlsanity.com/paper/1904.09545/) (2019)
- [MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark](https://mlsanity.com/paper/2008.09335/) (2020)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Towards Zero-Label Language Learning](https://mlsanity.com/paper/2109.09193/) (2021)
- [Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2001.11458/) (2020)
- [AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model](https://mlsanity.com/paper/2208.01448/) (2022)
- [Cross-lingual Retrieval for Iterative Self-Supervised Training](https://mlsanity.com/paper/2006.09526/) (2020)
- [Neural Data Augmentation via Example Extrapolation](https://mlsanity.com/paper/2102.01335/) (2021)
- [Data Augmentation for Intent Classification with Off-the-shelf Large Language Models](https://mlsanity.com/paper/2204.01959/) (2022)
- [LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging](https://mlsanity.com/paper/2209.09900/) (2022)
- [Transforming Sequence Tagging Into A Seq2Seq Task](https://mlsanity.com/paper/2203.08378/) (2022)
- [Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation](https://mlsanity.com/paper/2205.08675/) (2022)
- [Training Naturalized Semantic Parsers with Very Little Data](https://mlsanity.com/paper/2204.14243/) (2022)

## Built on by (most cited first, 21 of 21)

- [PLACES: Prompting Language Models for Social Conversation Synthesis](https://mlsanity.com/paper/2302.03269/) (2023)
- [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://mlsanity.com/paper/2404.00579/) (2024)
- [FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering](https://mlsanity.com/paper/2308.12060/) (2023)
- [Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers](https://mlsanity.com/paper/2404.04925/) (2024)
- [Generating Data for Symbolic Language with Large Language Models](https://mlsanity.com/paper/2305.13917/) (2023)
- [LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text](https://mlsanity.com/paper/2402.04335/) (2024)
- [Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks](https://mlsanity.com/paper/2212.09912/) (2022)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Grounding Data Science Code Generation with Input-Output Specifications](https://mlsanity.com/paper/2402.08073/) (2024)
- [Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation](https://mlsanity.com/paper/2404.00361/) (2024)
- [Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models](https://mlsanity.com/paper/2302.03491/) (2023)
- [Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2307.04096/) (2023)
- [TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text](https://mlsanity.com/paper/2410.21479/) (2024)
- [Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models](https://mlsanity.com/paper/2306.08756/) (2023)
- [Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection](https://mlsanity.com/paper/2311.02025/) (2023)
- [SpeechDialogueFactory: Generating High-Quality Speech Dialogue Data to Accelerate Your Speech-LLM Development](https://mlsanity.com/paper/2503.23848/) (2025)
- [NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models](https://mlsanity.com/paper/2510.09355/) (2025)
- [(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis](https://mlsanity.com/paper/2304.11473/) (2023)
- [Better Python Programming for all: With the focus on Maintainability](https://mlsanity.com/paper/2408.09134/) (2024)
- [Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing](https://mlsanity.com/paper/2410.00513/) (2024)
- [CALICO: Conversational Agent Localization via Synthetic Data Generation](https://mlsanity.com/paper/2412.05388/) (2024)

## Abstract (arXiv)

A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data. Given the complexity and cost of human annotation for SP, labeled data is often scarce, particularly in multilingual settings. Large Language Models (LLMs) excel at SP given only a few examples, however LLMs are unsuitable for runtime systems which require low latency. In this work, we propose CLASP, a simple method to improve low-resource SP for moderate-sized models: we generate synthetic data from AlexaTM 20B to augment the training set for a model 40x smaller (500M parameters). We evaluate on two datasets in low-resource settings: English PIZZA, containing either 348 or 16 real examples, and mTOP cross-lingual zero-shot, where training data is available only in English, and the model must generalize to four new languages. On both datasets, we show significant improvements over strong baseline methods.