# Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations arXiv: https://arxiv.org/abs/2404.12698 ยท HTML: https://mlsanity.com/paper/2404.12698/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 92 references that are in the map. - First posted on arXiv 19 Apr 2024. ## Builds on (most cited first, 30 of 32) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020) - [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016) - [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016) - [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020) - [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020) - [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021) - [COGS: A Compositional Generalization Challenge Based on Semantic Interpretation](https://mlsanity.com/paper/2010.05465/) (2020) - [Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks](https://mlsanity.com/paper/1912.09393/) (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) - [An Incremental Parser for Abstract Meaning Representation](https://mlsanity.com/paper/1608.06111/) (2016) - [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021) - [Core Semantic First: A Top-down Approach for AMR Parsing](https://mlsanity.com/paper/1909.04303/) (2019) - [AMR Similarity Metrics from Principles](https://mlsanity.com/paper/2001.10929/) (2020) - [SLOG: A Structural Generalization Benchmark for Semantic Parsing](https://mlsanity.com/paper/2310.15040/) (2023) - [SMATCH++: Standardized and Extended Evaluation of Semantic Graphs](https://mlsanity.com/paper/2305.06993/) (2023) - [AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes](https://mlsanity.com/paper/1507.01127/) (2015) - [Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations](https://mlsanity.com/paper/1705.09980/) (2017) - [Exploring Neural Methods for Parsing Discourse Representation Structures](https://mlsanity.com/paper/1810.12579/) (2018) - [Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation](https://mlsanity.com/paper/2302.14220/) (2023) - ['UFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERIN](https://mlsanity.com/paper/2011.00758/) (2020) - [RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy](https://mlsanity.com/paper/1604.01278/) (2016) - [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) - [AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite](https://mlsanity.com/paper/2312.03480/) (2023) - [PMB5: Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks](https://mlsanity.com/paper/2404.08354/) (2024) - [Compressing Word Embeddings](https://mlsanity.com/paper/1511.06397/) (2015) ## Built on by (most cited first, 6 of 6) - [Multilingual LLMs Struggle to Link Orthography and Semantics in Bilingual Word Processing](https://mlsanity.com/paper/2501.09127/) (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) - [Is neural semantic parsing good at ellipsis resolution, or isn't it?](https://mlsanity.com/paper/2508.00121/) (2025) - [Language Models Can Resolve Reference Compositionally, But It's Not Their Native Strength: The Case of the Personal Relation Task](https://mlsanity.com/paper/2605.31480/) (2026) - [Structural Automata for Neural Semantic Parsing](https://mlsanity.com/paper/2610.13896/) (2026) ## Abstract (arXiv) Current open-domain neural semantics parsers show impressive performance. However, closer inspection of the symbolic meaning representations they produce reveals significant weaknesses: sometimes they tend to merely copy character sequences from the source text to form symbolic concepts, defaulting to the most frequent word sense based in the training distribution. By leveraging the hierarchical structure of a lexical ontology, we introduce a novel compositional symbolic representation for concepts based on their position in the taxonomical hierarchy. This representation provides richer semantic information and enhances interpretability. We introduce a neural "taxonomical" semantic parser to utilize this new representation system of predicates, and compare it with a standard neural semantic parser trained on the traditional meaning representation format, employing a novel challenge set and evaluation metric for evaluation. Our experimental findings demonstrate that the taxonomical model, trained on much richer and complex meaning representations, is slightly subordinate in performance to the traditional model using the standard metrics for evaluation, but outperforms it when dealing with out-of-vocabulary concepts. This finding is encouraging for research in computational semantics that aims to combine data-driven distributional meanings with knowledge-based symbolic representations.