SOTAVerified

Semantic Parsing

Semantic Parsing is the task of transducing natural language utterances into formal meaning representations. The target meaning representations can be defined according to a wide variety of formalisms. This include linguistically-motivated semantic representations that are designed to capture the meaning of any sentence such as λ-calculus or the abstract meaning representations. Alternatively, for more task-driven approaches to Semantic Parsing, it is common for meaning representations to represent executable programs such as SQL queries, robotic commands, smart phone instructions, and even general-purpose programming languages like Python and Java.

Source: Tranx: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

Papers

Showing 151200 of 1202 papers

TitleStatusHype
OpenICL: An Open-Source Framework for In-context LearningCode2
NLP Workbench: Efficient and Extensible Integration of State-of-the-art Text Mining ToolsCode0
Is Japanese CCGBank empirically correct? A case study of passive and causative constructions0
PAC Prediction Sets for Large Language Models of CodeCode0
LEVER: Learning to Verify Language-to-Code Generation with ExecutionCode1
NL2CMD: An Updated Workflow for Natural Language to Bash Commands TranslationCode1
On graph-based reentrancy-free semantic parsing0
The Role of Semantic Parsing in Understanding Procedural TextCode0
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLCode2
Compositional Exemplars for In-context LearningCode1
Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based ReasoningCode1
Active Learning for Multilingual Semantic Parser0
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex0
Semantic Parsing for Conversational Question Answering over Knowledge GraphsCode1
Underwater Robotics Semantic Parser AssistantCode0
Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty QuantificationCode0
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)0
Low-Resource Compositional Semantic Parsing with Concept Pretraining0
Semantic-aware Contrastive Learning for More Accurate Semantic Parsing0
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL ParsingCode0
Structured Case-based Reasoning for Inference-time Adaptation of Text-to-SQL parsers0
Towards Autoformalization of Mathematics and Code Correctness: Experiments with Elementary ProofsCode0
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic KnowledgeCode0
OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided MixupCode1
Semantic Operator Prediction and Applications0
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic ParsingCode0
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction0
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models0
Extrinsic Evaluation of Machine Translation Metrics0
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs0
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented DialogueCode0
Multi-VALUE: A Framework for Cross-Dialectal English NLP0
Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing0
Diverse Demonstrations Improve In-context Compositional GeneralizationCode1
A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoods0
PhraseTransformer: An Incorporation of Local Context Information into Sequence-to-sequence Semantic ParsingCode0
Lexicon-injected Semantic Parsing for Task-Oriented Dialog0
Leveraging Data Recasting to Enhance Tabular Reasoning0
On the Compositional Generalization Gap of In-Context Learning0
Calibrated Interpretation: Confidence Estimation in Semantic ParsingCode1
CST5: Data Augmentation for Code-Switched Semantic ParsingCode1
Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database0
Toward a Neural Semantic Parsing System for EHR Question Answering0
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts0
Visual Semantic Parsing: From Images to Abstract Meaning Representation0
XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic ParsingCode0
ReaRev: Adaptive Reasoning for Question Answering over Knowledge GraphsCode1
Structural generalization is hard for sequence-to-sequence modelsCode0
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning ExamplesCode1
Shift-Reduce Task-Oriented Semantic Parsing with Stack-TransformersCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ARTEMIS-DAAccuracy (Test)80.8Unverified
2SynTQA (Oracle)Test Accuracy77.5Unverified
3TabLaPAccuracy (Test)76.6Unverified
4SynTQA (GPT)Accuracy (Test)74.4Unverified
5Mix SCAccuracy (Test)73.6Unverified
6SynTQA (RF)Accuracy (Test)71.6Unverified
7CABINETAccuracy (Test)69.1Unverified
8NormTab+TabSQLifyAccuracy (Test)68.63Unverified
9Chain-of-TableAccuracy (Test)67.31Unverified
10Tab-PoTAccuracy (Test)66.78Unverified
#ModelMetricClaimedVerifiedStatus
1RESDSQL-3B + NatSQLAccuracy84.1Unverified
2code-davinci-002 175B (LEVER)Accuracy81.9Unverified
3RASAT+PICARDAccuracy75.5Unverified
4Graphix-3B + PICARDAccuracy74Unverified
5T5-3B + PICARDAccuracy71.9Unverified
6SADGA + GAPAccuracy70.1Unverified
7RATSQL + GAPAccuracy69.7Unverified
8RATSQL + Grammar-Augmented Pre-TrainingAccuracy69.6Unverified
9RATSQL + BERTAccuracy65.6Unverified
10Exact Set MatchingAccuracy19.7Unverified
#ModelMetricClaimedVerifiedStatus
1Dynamic Least-to-Most PromptingExact Match95Unverified
2LeARExact Match90.9Unverified
3T5-3B w/ Intermediate RepresentationsExact Match83.8Unverified
4Hierarchical Poset DecodingExact Match69Unverified
5Universal TransformerExact Match18.9Unverified
#ModelMetricClaimedVerifiedStatus
1ReaRevAccuracy76.4Unverified
2NSM+hAccuracy74.3Unverified
3CBR-KBQAAccuracy70Unverified
4STAGG (Yih et al., 2016)Accuracy63.9Unverified
5T5-11B (Raffel et al., 2020)Accuracy56.5Unverified
#ModelMetricClaimedVerifiedStatus
1CABINETDenotation accuracy (test)89.5Unverified
2TAPEX-Large (weak supervision)Denotation accuracy (test)89.5Unverified
3ReasTAP-Large (weak supervision)Denotation accuracy (test)89.2Unverified
4NL2SQL-BERTAccuracy89Unverified
5TAPAS-Large (weak supervision)Denotation accuracy (test)83.6Unverified
#ModelMetricClaimedVerifiedStatus
1PhraseTransformerAccuracy90.4Unverified
2TranxAccuracy86.2Unverified
3ASN (Rabinovich et al., 2017)Accuracy85.3Unverified
4ZH15 (Zhao and Huang, 2015)Accuracy84.2Unverified
#ModelMetricClaimedVerifiedStatus
1coarse2fineAccuracy88.2Unverified
2PhraseTransformerAccuracy87.9Unverified
3TranxAccuracy87.7Unverified
#ModelMetricClaimedVerifiedStatus
1PERIN + RobeCzechF192.36Unverified
2PERINF192.24Unverified
3HUJI-KUF158Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF180.52Unverified
2HUJI-KUF145Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF180.23Unverified
2HUJI-KUF152Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF194.16Unverified
2HUJI-KUF163Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF189.83Unverified
2HUJI-KUF162Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF192.73Unverified
2HUJI-KUF180Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF189.19Unverified
2HUJI-KUF154Unverified
#ModelMetricClaimedVerifiedStatus
1TAPEX-LargeDenotation Accuracy74.5Unverified
2TAPAS-LargeAccuracy67.2Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF176.4Unverified
2HUJI-KUF173Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF181.01Unverified
2HUJI-KUF175Unverified
#ModelMetricClaimedVerifiedStatus
1HSPEM66.18Unverified
#ModelMetricClaimedVerifiedStatus
1ReasonBERTRF1 Score41.3Unverified
#ModelMetricClaimedVerifiedStatus
1MeMCEExact40.3Unverified