# MACT: Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing

arXiv: https://arxiv.org/abs/2406.01052 · HTML: https://mlsanity.com/paper/2406.01052/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 58 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 28 of 28)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Towards a Unified View of Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2110.04366/) (2021)
- [Few-shot Learning with Multilingual Language Models](https://mlsanity.com/paper/2112.10668/) (2021)
- [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://mlsanity.com/paper/1908.11828/) (2019)
- [Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning](https://mlsanity.com/paper/2109.05687/) (2021)
- [Composable Sparse Fine-Tuning for Cross-Lingual Transfer](https://mlsanity.com/paper/2110.07560/) (2021)
- [mGPT: Few-Shot Learners Go Multilingual](https://mlsanity.com/paper/2204.07580/) (2022)
- [Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks](https://mlsanity.com/paper/1909.00964/) (2019)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://mlsanity.com/paper/1904.02099/) (2019)
- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations](https://mlsanity.com/paper/1702.03964/) (2017)
- [Cross-lingual Abstract Meaning Representation Parsing](https://mlsanity.com/paper/1704.04539/) (2017)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [Exploring Neural Methods for Parsing Discourse Representation Structures](https://mlsanity.com/paper/1810.12579/) (2018)
- [Cross-lingual Annotation Projection for Semantic Roles](https://mlsanity.com/paper/1401.5694/) (2014)
- [Evaluating Scoped Meaning Representations](https://mlsanity.com/paper/1802.08599/) (2018)
- [Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation](https://mlsanity.com/paper/2306.00124/) (2023)
- [Parameter sharing between dependency parsers for related languages](https://mlsanity.com/paper/1808.09055/) (2018)
- [XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations](https://mlsanity.com/paper/2306.04085/) (2023)
- [One model, two languages: training bilingual parsers with harmonized treebanks](https://mlsanity.com/paper/1507.08449/) (2015)

## Built on by (most cited first, 3 of 3)

- [Hire Your Anthropologist! Rethinking Culture Benchmarks Through an Anthropological Lens](https://mlsanity.com/paper/2510.05931/) (2025)
- [Retrieval-Augmented Semantic Parsing: Improving Generalization with Lexical Knowledge](https://mlsanity.com/paper/2412.10207/) (2024)
- [Multi-Modal Semantic Parsing for the Interpretation of Tombstone Inscriptions](https://mlsanity.com/paper/2507.04377/) (2025)

## Abstract (arXiv)

Discourse Representation Structure (DRS) is an innovative semantic representation designed to capture the meaning of texts with arbitrary lengths across languages. The semantic representation parsing is essential for achieving natural language understanding through logical forms. Nevertheless, the performance of DRS parsing models remains constrained when trained exclusively on monolingual data. To tackle this issue, we introduce a cross-lingual training strategy. The proposed method is model-agnostic yet highly effective. It leverages cross-lingual training data and fully exploits the alignments between languages encoded in pre-trained language models. The experiments conducted on the standard benchmarks demonstrate that models trained using the cross-lingual training method exhibit significant improvements in DRS clause and graph parsing in English, German, Italian and Dutch. Comparing our final models to previous works, we achieve state-of-the-art results in the standard benchmarks. Furthermore, the detailed analysis provides deep insights into the performance of the parsers, offering inspiration for future research in DRS parsing. We keep updating new results on benchmarks to the appendix.