# The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations

arXiv: https://arxiv.org/abs/1702.03964 · HTML: https://mlsanity.com/paper/1702.03964/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2017.

## Built on by (most cited first, 30 of 42)

- [Discovering Latent Concepts Learned in BERT](https://mlsanity.com/paper/2205.07237/) (2022)
- [Analyzing Encoded Concepts in Transformer Language Models](https://mlsanity.com/paper/2206.13289/) (2022)
- [PolyPythias: Stability and Outliers across Fifty Language Model Pre-Training Runs](https://mlsanity.com/paper/2503.09543/) (2025)
- [How transfer learning impacts linguistic knowledge in deep NLP models?](https://mlsanity.com/paper/2105.15179/) (2021)
- [Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training](https://mlsanity.com/paper/2310.16484/) (2023)
- [Discovering Salient Neurons in Deep NLP Models](https://mlsanity.com/paper/2206.13288/) (2022)
- [On the Transformation of Latent Space in Fine-Tuned NLP Models](https://mlsanity.com/paper/2210.12696/) (2022)
- [Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora](https://mlsanity.com/paper/2303.03750/) (2023)
- [Scaling up Discovery of Latent Concepts in Deep NLP Models](https://mlsanity.com/paper/2308.10263/) (2023)
- [Evaluating Neuron Interpretation Methods of NLP Models](https://mlsanity.com/paper/2301.12608/) (2023)
- [Towards General Natural Language Understanding with Probabilistic Worldbuilding](https://mlsanity.com/paper/2105.02486/) (2021)
- [Multilingual Neural Semantic Parsing for Low-Resourced Languages](https://mlsanity.com/paper/2106.03469/) (2021)
- [Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations](https://mlsanity.com/paper/2404.12698/) (2024)
- [PMB5: Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks](https://mlsanity.com/paper/2404.08354/) (2024)
- [How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics](https://mlsanity.com/paper/2410.03429/) (2024)
- [One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets](https://mlsanity.com/paper/2106.04476/) (2021)
- [Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization](https://mlsanity.com/paper/2305.12169/) (2023)
- [Discourse Representation Structure Parsing for Chinese](https://mlsanity.com/paper/2306.09725/) (2023)
- [A Pipeline For Discourse Circuits From CCG](https://mlsanity.com/paper/2311.17892/) (2023)
- [MACT: Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing](https://mlsanity.com/paper/2406.01052/) (2024)
- [Scope-enhanced Compositional Semantic Parsing for DRT](https://mlsanity.com/paper/2407.01899/) (2024)
- [Retrieval-Augmented Semantic Parsing: Improving Generalization with Lexical Knowledge](https://mlsanity.com/paper/2412.10207/) (2024)
- [Form and Meaning in Intrinsic Multilingual Evaluations](https://mlsanity.com/paper/2601.10580/) (2026)
- [Towards Property-Based Tests in Natural Language](https://mlsanity.com/paper/2202.03616/) (2022)
- [Natural Language Specifications in Proof Assistants](https://mlsanity.com/paper/2205.07811/) (2022)
- [Task-Oriented Paraphrase Analytics](https://mlsanity.com/paper/2403.17564/) (2024)
- [MetaphorShare: A Dynamic Collaborative Repository of Open Metaphor Datasets](https://mlsanity.com/paper/2411.18260/) (2024)
- [From Words to Waves: Analyzing Concept Formation in Speech and Text-Based Foundation Models](https://mlsanity.com/paper/2506.01133/) (2025)
- [Evaluating the Impact of Verbal Multiword Expressions on Machine Translation](https://mlsanity.com/paper/2508.17458/) (2025)
- [A Theorem-Proving-Based Evaluation of Neural Semantic Parsing](https://mlsanity.com/paper/2510.11225/) (2025)

## Abstract (arXiv)

The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Italian, and Dutch). Our approach is based on cross-lingual projection: automatically produced (and manually corrected) semantic annotations for English sentences are mapped onto their word-aligned translations, assuming that the translations are meaning-preserving. The semantic annotation consists of five main steps: (i) segmentation of the text in sentences and lexical items; (ii) syntactic parsing with Combinatory Categorial Grammar; (iii) universal semantic tagging; (iv) symbolization; and (v) compositional semantic analysis based on Discourse Representation Theory. These steps are performed using statistical models trained in a semi-supervised manner. The employed annotation models are all language-neutral. Our first results are promising.