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 851900 of 1202 papers

TitleStatusHype
Embedded Semantic Lexicon Induction with Joint Global and Local Optimization0
Semantic Frames and Visual Scenes: Learning Semantic Role Inventories from Image and Video Descriptions0
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter0
Macro Grammars and Holistic Triggering for Efficient Semantic ParsingCode0
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds0
Evaluating Semantic Parsing against a Simple Web-based Question Answering ModelCode0
Towards Zero-Shot Frame Semantic Parsing for Domain ScalingCode1
Domain Specific Automatic Question Generation from Text0
WebChild 2.0 : Fine-Grained Commonsense Knowledge Distillation0
NLP for Precision Medicine0
Fast Forward Through Opportunistic Incremental Meaning Representation Construction0
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge0
Search-based Neural Structured Learning for Sequential Question Answering0
Neural Architectures for Multilingual Semantic Parsing0
Semantic Parsing of Pre-university Math Problems0
Generic Axiomatization of Families of Noncrossing Graphs in Dependency ParsingCode0
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic ScaffoldCode1
Transfer Learning for Neural Semantic Parsing0
Generic Axiomatization of Families of Noncrossing Graphs in Dependency ParsingCode0
Function Assistant: A Tool for NL Querying of APIsCode0
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning RepresentationsCode0
Learning Semantic Correspondences in Technical Documentation0
Logical Parsing from Natural Language Based on a Neural Translation Model0
Learning Structured Natural Language Representations for Semantic Parsing0
Abstract Syntax Networks for Code Generation and Semantic ParsingCode0
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal LikelihoodCode0
Cross-domain Semantic Parsing via ParaphrasingCode0
Generative Face CompletionCode0
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing0
A Syntactic Neural Model for General-Purpose Code GenerationCode0
A Transition-Based Directed Acyclic Graph Parser for UCCACode0
Integer Linear Programming formulations in Natural Language Processing0
Imitation learning for structured prediction in natural language processing0
The SUMMA Platform Prototype0
An Extensible Framework for Verification of Numerical Claims0
COVER: Covering the Semantically Tractable Questions0
Alto: Rapid Prototyping for Parsing and Translation0
Paraphrasing Revisited with Neural Machine Translation0
Integrated Learning of Dialog Strategies and Semantic Parsing0
MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models0
Universal Semantic ParsingCode0
Neural Semantic Parsing over Multiple Knowledge-basesCode0
A constrained graph algebra for semantic parsing with AMRs0
Annotating and parsing to semantic frames: feedback from the French FrameNet project0
Project Notes on building a conversational parser on top of a text parser: Towards a causal language tagger for spoken Chinese0
Indexicals and Compositionality: Inside-Out or Outside-In?0
Automatically Tagging Constructions of Causation and Their Slot-Fillers0
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)0
PointNet: Deep Learning on Point Sets for 3D Classification and SegmentationCode1
Knowledge-Driven Event Embedding for Stock Prediction0
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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