Papers with MRs
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation (D18-2)
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| Challenge: | Existing neural semantic parsers only focus on a small subset of tasks, such as SQL queries, robotic commands, and even general-purpose programming languages like Java. |
| Approach: | They propose a transition-based neural semantic parser that maps natural language utterances into formal meaning representations (MRs) they use an abstract syntax description language to constrain the output space and model the information flow. |
| Outcome: | Experiments on four different semantic parsing and code generation tasks show that the proposed system is generalizable, extensible, and effective. |
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (P18-1)
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| Challenge: | Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. |
| Approach: | They propose a variational auto-encoding model for semi-supervised semantic parsing which learns from limited amounts of parallel data and readily-available unlabeled NL utterances. |
| Outcome: | Experiments on ATIS domain and Python show that with extra unlabeled data, StructVAE outperforms strong supervised models. |
Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization (P19-1)
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| Challenge: | Semantic parsing aims to transform natural language utterances into formal meaning representations (MRs) whereas an NL generator achieves the reverse, the two tasks are often studied separately. |
| Approach: | They propose a method of dual information maximization to regularize the learning process by matching the joint distributions of p and q of NLs. |
| Outcome: | The proposed method empirically maximizes the variational lower bounds of expected joint distributions of NL and MRs. |
Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for self-training from meaning representations (MRs) are noisy or uninformative for the model to learn from. |
| Approach: | They propose a two-phase procedure to generate high-quality pseudo-labeled MR-to-Text pairs by aggregating multiple perturbed latent representations from each MR. |
| Outcome: | Empirical results on two benchmark datasets show that the proposed procedure outperforms existing methods on automatic and human evaluations. |
Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing (2022.emnlp-main)
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| Challenge: | Existing studies show that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser. |
| Approach: | They propose a data-aware metric called ISS to measure the final performance of MRs. |
| Outcome: | The proposed metric denoting incremental structural stability (ISS) of MRs can be used as an indicator for MR design to avoid the costly training-testing process. |
Normalizing Compositional Structures Across Graphbanks (2020.coling-main)
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| Challenge: | Graph-based meaning representations (MRs) exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. |
| Approach: | They propose a method to normalize MRs at the compositional level by linguistically-grounded rules. |
| Outcome: | The proposed method increases the match in compositional structure between MRs and improves multi-task learning in a low-resource setting. |
Improving Compositional Generalization with Self-Training for Data-to-Text Generation (2022.acl-long)
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| Challenge: | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). |
| Approach: | They propose a template-based input representation that greatly improves the model’s generalization capability. |
| Outcome: | The proposed model improves tree accuracy by 46%+ and reduces slot error rates by 73%+ over the strong baselines on SGD and Weather benchmarks. |
Reranking for Neural Semantic Parsing (P19-1)
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| Challenge: | Semantic parsing is the task of transducing natural language utterances into machine executable meaning representations (e.g., Python code). |
| Approach: | They propose to rerank an n-best list of predicted MRs and use features to fix observed problems with baseline models to improve parser performance. |
| Outcome: | The proposed method outperforms the best published neural parser on four datasets and improves the baseline parsing performance by 5.7% and 2.9%. |
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. |