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 901950 of 1202 papers

TitleStatusHype
Multilingual Supervision of Semantic Annotation0
Hybrid Question Answering over Knowledge Base and Free Text0
Multilingual Information Extraction with PolyglotIE0
Evaluation Strategies for Computational Construction Grammars0
Splitting compounds with ngrams0
Deeper syntax for better semantic parsing0
Cross-lingual Learning of an Open-domain Semantic Parser0
QAF: Frame Semantics-based Question Interpretation0
Improving word alignment for low resource languages using English monolingual SRL0
From Entity Linking to Question Answering -- Recent Progress on Semantic Grounding Tasks0
Semantic Parsing of Mathematics by Context-based Learning from Aligned Corpora and Theorem Proving0
Answering Complicated Question Intents Expressed in Decomposed Question Sequences0
A Neural Model for Language Identification in Code-Switched Tweets0
Towards Broad-coverage Meaning Representation: The Case of Comparison Structures0
Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser0
Improving Semantic Parsing via Answer Type Inference0
Equation Parsing : Mapping Sentences to Grounded Equations0
Neural Shift-Reduce CCG Semantic Parsing0
Computational linking theory0
Semantic Parsing with Semi-Supervised Sequential Autoencoders0
Evaluating Induced CCG Parsers on Grounded Semantic ParsingCode0
Semantic Tagging with Deep Residual NetworksCode0
The aNALoGuE Challenge: Non Aligned Language GEneration0
Crowd-sourcing NLG Data: Pictures Elicit Better Data.0
Detailed Garment Recovery from a Single-View Image0
Interactively Learning Visually Grounded Word Meanings from a Human Tutor0
A Corpus of Preposition Supersenses0
Learning to Jointly Predict Ellipsis and Comparison Structures0
ccg2lambda: A Compositional Semantics System0
Unanimous Prediction for 100\% Precision with Application to Learning Semantic Mappings0
The Value of Semantic Parse Labeling for Knowledge Base Question Answering0
Efficient techniques for parsing with tree automata0
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation0
Improved Semantic Parsers For If-Then Statements0
Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling0
Sequence-based Structured Prediction for Semantic Parsing0
Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time0
Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal KnowledgeCode0
Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMsCode0
Semantic Parsing to Probabilistic Programs for Situated Question Answering0
The Role of CNL and AMR in Scalable Abstractive Summarization for Multilingual Media Monitoring0
Unanimous Prediction for 100% Precision with Application to Learning Semantic MappingsCode0
A Probabilistic Generative Grammar for Semantic ParsingCode0
Simpler Context-Dependent Logical Forms via Model ProjectionsCode0
Data Recombination for Neural Semantic ParsingCode0
Learning Language Games through InteractionCode0
3D Semantic Parsing of Large-Scale Indoor Spaces0
Hyperedge Replacement and Nonprojective Dependency Structures0
Detecting novel metaphor using selectional preference information0
Paraphrase for Open Question Answering: New Dataset and Methods0
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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