Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained models excel at graph semantic parsing with rich annotated data, but generalize poorly to out-of-distribution and long-tail examples. |
| Approach: | They propose a compositionality-aware approach to neural-symbolic inference informed by model confidence to capture different aspects of the graph prediction. |
| Outcome: | The proposed method outperforms state-of-the-art models on an English resource grammar parsing problem on standard in-domain and seven OOD corpora. |
Similar Papers
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty (2022.findings-acl)
Copied to clipboard
| Challenge: | Recent work in task-independent graph semantic parsing has shifted from symbolic approaches to neural models, showing strong performance on different types of meaning representations. |
| Approach: | They propose a framework that incorporates prior knowledge from a symbolic parser into a decision criterion for beam search to address these limitations. |
| Outcome: | The proposed framework improves on the in-distribution test set but degrades significantly on long-tail situations while the symbolic parser performs more robustly. |
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Neural network models suffer from performance losses when faced with compositionally out-of-distribution data. |
| Approach: | They propose to use neural semantic parsers to detect compositionally out-of-distribution (OOD) data. |
| Outcome: | The proposed methods perform well on the standard SCAN and CFQ datasets. |
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing (2023.acl-long)
Copied to clipboard
Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury, Tahira Naseem, Ramon Fernandez Astudillo, Achille Fokoue, Tim Klinger
| Challenge: | Compositional generalization is a key feature of human intelligence and has been identified as a major point of weakness in neural methods for semantic parsing. |
| Approach: | They propose a neural parsing generation method that constructs logical forms from the bottom up, beginning from the logical form’s leaves. |
| Outcome: | The proposed method outperforms general-purpose parsers on a CFQ dataset and two other Text-to-SQL datasets while also being competitive with parser that have been tailored to each task. |
Compositional Semantic Parsing across Graphbanks (P19-1)
Copied to clipboard
| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
| Approach: | They propose a compositional neural semantic parser which achieves competitive accuracies across graphbanks. |
| Outcome: | The proposed system achieves competitive accuracies across a variety of graphbanks. |
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning? (2023.eacl-main)
Copied to clipboard
Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui
| Challenge: | Using a pre-trained dataset, we examine how well recent neural models capture compositionality in symbolic reasoning tasks. |
| Approach: | They propose a skill tree on compositionality that defines hierarchical levels of complexity along with three compositionality dimensions: systematicity, productivity, and substitutivity. |
| Outcome: | The proposed model struggled most with systematicity, performing poorly even with relatively simple compositions. |
Syntax-Aware Retrieval Augmentation for Neural Symbolic Regression (2025.emnlp-main)
Copied to clipboard
| Challenge: | Symbolic regression is a powerful technique for discovering mathematical expressions that best fit observed data. |
| Approach: | They propose a syntax-aware retrieval-augmented mechanism that leverages syntactic structure of symbolic expressions to perform context-awful retrieval from a pre-constructed token datastore. |
| Outcome: | The proposed method outperforms representative baselines on symbolic regression benchmarks and is validated on multiple symbolic regression datasets. |
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way. |
| Approach: | They examine whether syntactic and semantic graph representations can complement and improve neural language modeling. |
| Outcome: | The proposed model outperforms pretrained models on English WSJ in perplexity and other metrics. |
Compositional Generalization via Semantic Tagging (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing neural sequence-to-sequence models fail at compositional generalization, i.e., they cannot generalize to unseen compositions of seen components. |
| Approach: | They propose a decoding framework that preserves expressivity and generality of sequence-to-sequence models while featuring lexicon-style alignments and disentangled information processing. |
| Outcome: | The proposed framework improves compositional generalization across model architectures, domains, and semantic formalisms on three semantic parsing datasets. |
AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing (P19-1)
Copied to clipboard
| Challenge: | Semantic parsing (SP) maps a natural language utterance into a formal language . standard Seq2Seq models ignore underlying grammars and may give ill-formed results. |
| Approach: | They propose an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation. |
| Outcome: | The proposed model outperforms the state-of-the-art models and does not need expertise like predefined grammar or sketches in the meantime. |
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog (2021.naacl-main)
Copied to clipboard
| Challenge: | Semantic parsing using sequence-to-sequence models is stymied by higher compute requirements and higher latency. |
| Approach: | They propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture. |
| Outcome: | The proposed architecture achieves an 81% reduction in latency on TOP dataset and retains competitive performance over non-pretrained models on three different semantic parsing datasets. |