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 751800 of 1202 papers

TitleStatusHype
Integer Linear Programming formulations in Natural Language Processing0
Integrated Learning of Dialog Strategies and Semantic Parsing0
Integrating Generative Lexicon Event Structures into VerbNet0
Intégration de tâches: étiquetage morpho-syntaxique, analyse syntaxique et analyse sémantique traités comme une tâche unique (Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task )0
Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection0
Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic0
Interactively Learning Visually Grounded Word Meanings from a Human Tutor0
Intermediary Semantic Representation through Proposition Structures0
Interpolated Convolutional Networks for 3D Point Cloud Understanding0
Interpreting Questions with a Log-Linear Ranking Model in a Virtual Patient Dialogue System0
Interpreting Situated Dialogue Utterances: an Update Model that Uses Speech, Gaze, and Gesture Information0
Is Japanese CCGBank empirically correct? A case study of passive and causative constructions0
Iterative Search for Weakly Supervised Semantic Parsing0
Iterative Utterance Segmentation for Neural Semantic Parsing0
Joint A* CCG Parsing and Semantic Role Labelling0
Joint Case Argument Identification for Japanese Predicate Argument Structure Analysis0
Joint Concept Learning and Semantic Parsing from Natural Language Explanations0
Joint learning of ontology and semantic parser from text0
Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling0
Joint Multi-Decoder Framework with Hierarchical Pointer Network for Frame Semantic Parsing0
Joint Relational Embeddings for Knowledge-based Question Answering0
Joint Syntactic and Semantic Parsing with Combinatory Categorial Grammar0
Joint Video and Text Parsing for Understanding Events and Answering Queries0
Kicktionary-LOME: A Domain-Specific Multilingual Frame Semantic Parsing Model for Football Language0
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning0
Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning0
Knowledge-Aware Conversational Semantic Parsing Over Web Tables0
Knowledge-Based Question Answering as Machine Translation0
Knowledge Base Question Answering: A Semantic Parsing Perspective0
Knowledge-Driven Event Embedding for Stock Prediction0
Knowledge Extraction and Joint Inference Using Tractable Markov Logic0
Knowledge Informed Semantic Parsing for Conversational Question Answering0
KUL-Eval: A Combinatory Categorial Grammar Approach for Improving Semantic Parsing of Robot Commands using Spatial Context0
L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models0
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing0
Lambda Dependency-Based Compositional Semantics0
Language Generation via DAG Transduction0
Language-Guided World Models: A Model-Based Approach to AI Control0
Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes0
Large-scale CCG Induction from the Groningen Meaning Bank0
Large-scale Semantic Parsing via Schema Matching and Lexicon Extension0
Large-scale Semantic Parsing without Question-Answer Pairs0
Latent Structure Models for Natural Language Processing0
Layers of Interpretation: On Grammar and Compositionality0
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing0
LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System0
Lean Question Answering over Freebase from Scratch0
Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary0
Learning Adaptive Language Interfaces through Decomposition0
Learning a Lexicon for Broad-coverage Semantic Parsing0
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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