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
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)0
QAF: Frame Semantics-based Question Interpretation0
From Entity Linking to Question Answering -- Recent Progress on Semantic Grounding Tasks0
Multilingual Supervision of Semantic Annotation0
Multilingual Information Extraction with PolyglotIE0
Hybrid Question Answering over Knowledge Base and Free Text0
Splitting compounds with ngrams0
Knowledge-Driven Event Embedding for Stock Prediction0
Deeper syntax for better semantic parsing0
Evaluation Strategies for Computational Construction Grammars0
Improving word alignment for low resource languages using English monolingual SRL0
Cross-lingual Learning of an Open-domain Semantic Parser0
Semantic Parsing of Mathematics by Context-based Learning from Aligned Corpora and Theorem Proving0
Answering Complicated Question Intents Expressed in Decomposed Question Sequences0
Equation Parsing : Mapping Sentences to Grounded Equations0
Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser0
Towards Broad-coverage Meaning Representation: The Case of Comparison Structures0
A Neural Model for Language Identification in Code-Switched Tweets0
Improving Semantic Parsing via Answer Type Inference0
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
Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling0
Unanimous Prediction for 100\% Precision with Application to Learning Semantic Mappings0
The Value of Semantic Parse Labeling for Knowledge Base Question Answering0
Improved Semantic Parsers For If-Then Statements0
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation0
Efficient techniques for parsing with tree automata0
A Corpus of Preposition Supersenses0
ccg2lambda: A Compositional Semantics System0
Learning to Jointly Predict Ellipsis and Comparison Structures0
Sequence-based Structured Prediction for Semantic Parsing0
Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time0
Interactively Learning Visually Grounded Word Meanings from a Human Tutor0
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
A Probabilistic Generative Grammar for Semantic ParsingCode0
Unanimous Prediction for 100% Precision with Application to Learning Semantic MappingsCode0
Simpler Context-Dependent Logical Forms via Model ProjectionsCode0
Data Recombination for Neural Semantic ParsingCode0
Learning Language Games through InteractionCode0
Paraphrase for Open Question Answering: New Dataset and Methods0
Neural Enquirer: Learning to Query Tables in Natural Language0
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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