# Multilingual Neural Semantic Parsing for Low-Resourced Languages

arXiv: https://arxiv.org/abs/2106.03469 · HTML: https://mlsanity.com/paper/2106.03469/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Coarse-to-Fine Decoding for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04793/) (2018)
- [Semantic Parsing for Task Oriented Dialog using Hierarchical Representations](https://mlsanity.com/paper/1810.07942/) (2018)
- [Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2001.11458/) (2020)
- [The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations](https://mlsanity.com/paper/1702.03964/) (2017)
- [Universal Semantic Parsing](https://mlsanity.com/paper/1702.03196/) (2017)
- [Cross-lingual Abstract Meaning Representation Parsing](https://mlsanity.com/paper/1704.04539/) (2017)
- [Bootstrapping a Crosslingual Semantic Parser](https://mlsanity.com/paper/2004.02585/) (2020)
- [Stronger Baselines for Trustable Results in Neural Machine Translation](https://mlsanity.com/paper/1706.09733/) (2017)

## Built on by (most cited first, 7 of 7)

- [Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems](https://mlsanity.com/paper/2206.07808/) (2022)
- [Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering](https://mlsanity.com/paper/2310.13583/) (2023)
- [Bootstrapping Multilingual Semantic Parsers using Large Language Models](https://mlsanity.com/paper/2210.07313/) (2022)
- [Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2307.04096/) (2023)
- [Evaluating Inter-Bilingual Semantic Parsing for Indian Languages](https://mlsanity.com/paper/2304.13005/) (2023)
- [Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing](https://mlsanity.com/paper/2410.00513/) (2024)
- [FineWeb-CLaR: Culture, Language, and Region Annotations for Benchmark-Aligned Corpus Auditing](https://mlsanity.com/paper/2609.25298/) (2026)

## Abstract (arXiv)

Multilingual semantic parsing is a cost-effective method that allows a single model to understand different languages. However, researchers face a great imbalance of availability of training data, with English being resource rich, and other languages having much less data. To tackle the data limitation problem, we propose using machine translation to bootstrap multilingual training data from the more abundant English data. To compensate for the data quality of machine translated training data, we utilize transfer learning from pretrained multilingual encoders to further improve the model. To evaluate our multilingual models on human-written sentences as opposed to machine translated ones, we introduce a new multilingual semantic parsing dataset in English, Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset and outperforms the state-of-the-art model on the public NLMaps dataset. We also establish a new baseline for zero-shot learning on the TOP dataset. We find that a semantic parser trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.