Papers with meaning representations
Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
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| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics (2021.findings-acl)
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| Challenge: | Existing models have limitations to generalize to diverse semantic phenomena, and it is unclear whether they can capture compositional meanings. |
| Approach: | They propose a systematic generalization testbed based on Natural language semantics to map natural language sentences to multiple meaning representations. |
| Outcome: | The proposed model can generalize to unseen combinations of quantifiers, negations, and modifiers, but not to the others. |
Measuring Alignment Bias in Neural Seq2seq Semantic Parsers (2022.starsem-1)
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| Challenge: | Sequence-to-sequence semantic parsers with attention mechanisms have changed the research landscape . emergence of seq2seq models have led to questions about alignments . |
| Approach: | They investigate whether seq2seq models can handle both simple and complex alignments. |
| Outcome: | The proposed model performs better on monotonic and complex alignments compared to monotonic models . |
Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing (2023.findings-eacl)
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| Challenge: | Existing approaches that model alignments between sentences fail at compositional generalization tasks, resulting in a resurgence of such approaches. |
| Approach: | They propose a two-step approach that first translates input sentences monotonically and then reorders them to obtain the correct output. |
| Outcome: | The proposed approach improves compositional generalization over existing models and other approaches that exploit gold alignment annotations. |
Incorporating EDS Graph for AMR Parsing (2021.starsem-1)
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| Challenge: | AMR is abstract and conceptual, while EDS is low level, closer to the lexical structures of the given sentences. |
| Approach: | They propose to add EDS graphs as additional semantic features to AMR parsers by adding transition-based parser to add LSTM layer and GCN layer. |
| Outcome: | The proposed parser adds EDS graphs as additional semantic features to boost performance . Currently the parsing accuracies for AMR are in low 80s, while they can be improved by adding more information from EDS. |
Align and Augment: Generative Data Augmentation for Compositional Generalization (2024.eacl-long)
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| Challenge: | Recent work on semantic parsing has shown that seq2seq models find compositional generalization challenging. |
| Approach: | They propose a data-augmentation strategy that exploits alignment annotations between sentences and their corresponding meaning representations to improve compositional generalization. |
| Outcome: | The proposed model improves compositional generalization performance by exploiting alignment annotations between sentences and their corresponding meaning representations. |
Promoting Graph Awareness in Linearized Graph-to-Text Generation (2021.findings-acl)
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| Challenge: | Recent applications of pretrained transformers to linearizations of graph inputs yield stateof-the-art results on graph-to-text tasks. |
| Approach: | They propose to use pretrained transformers to encode local graph structures . they find they can improve the quality of models' implicit graph encodings . |
| Outcome: | The proposed models can encode local graph structures and reconstruct corrupted inputs. |
Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing work on meaning representations is not comprehensively evaluated due to the lack of readily-available execution engines. |
| Approach: | They propose a unified benchmark on meaning representations by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. |
| Outcome: | The proposed benchmark combines existing parsing datasets, completes missing logical forms, and implements missing execution engines. |
Hybrid Semantics for Goal-Directed Natural Language Generation (2022.acl-long)
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| Challenge: | Existing goal-directed natural language generation systems use first-order logic to represent semantics, but they are often slow due to the semantics of the partially realized text being checked. |
| Approach: | They propose to use logical semantics and distributional semantics to combine meaning representations to scale a goal-directed natural language generation system without losing expressiveness. |
| Outcome: | The proposed approach scales significantly better than the goal-directed generation system, but it is slower because the representations are not as precise as pure logical semantics. |
Semantic-aware Contrastive Learning for More Accurate Semantic Parsing (2022.emnlp-main)
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| Challenge: | Existing studies on semantic parsing use Maximum Likelihood Estimation (MLE) to train discriminative semantic parses. |
| Approach: | They propose a semantic-aware contrastive learning algorithm which can learn to distinguish fine-grained meaning representations and take the overall sequence-level semantic into consideration. |
| Outcome: | The proposed algorithm improves on two standard datasets and gets state-of-the-art performance over existing methods. |
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty (2022.findings-acl)
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| 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. |
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have been used for tasks in computational semantics but meaning representations are not included in PLMs. |
| Approach: | They propose to include meaning representations besides natural language texts in the same model . they propose to use DRSs to improve performance of non-English tasks . |
| Outcome: | The proposed approach achieves the best performance on multilingual parsing and DRS-to-text generation tasks. |
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations (2023.acl-long)
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| Challenge: | Existing models for cross-lingual semantic parsing are not able to perform tasks on a wide range of datasets. |
| Approach: | They propose a benchmark for cross-lingual semantic parsing that uses 22 natural languages and 8 meaning representations to translate queries into MRs. |
| Outcome: | The proposed benchmarks cover 22 natural languages and 8 meaning representations on 164 domains and 5 tasks covering a wide range of multilingual language models. |