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 351–400 of 1202 papers

TitleStatusHype
From Natural Language Instructions to Complex Processes: Issues in Chaining Trigger Action Rules—0
Self-Enhancing Multi-filter Sequence-to-Sequence Model—0
From Treebank Parses to Episodic Logic and Commonsense Inference—0
Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection—0
Enhancing The RATP-DECODA Corpus With Linguistic Annotations For Performing A Large Range Of NLP Tasks—0
Classifying Temporal Relations with Simple Features—0
Equation Parsing : Mapping Sentences to Grounded Equations—0
Error-Aware Interactive Semantic Parsing of OpenStreetMap—0
A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoods—0
ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation—0
Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing—0
EUSP: An Easy-to-Use Semantic Parsing PlatForm—0
Generating Logical Forms from Graph Representations of Text and Entities—0
Graph parsing with s-graph grammars—0
Distilling Large Language Models into Tiny and Effective Students using pQRNN—0
Evaluating Byte and Wordpiece Level Models for Massively Multilingual Semantic Parsing—0
Disentangled Sequence to Sequence Learning for Compositional Generalization—0
Breeding Fillmore’s Chickens and Hatching the Eggs: Recombining Frames and Roles in Frame-Semantic Parsing—0
Annotation Schemes for Surface Construction Labeling—0
Combining Formal and Distributional Models of Temporal and Intensional Semantics—0
Annotating and parsing to semantic frames: feedback from the French FrameNet project—0
Combining Improvements for Exploiting Dependency Trees in Neural Semantic Parsing—0
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing—0
A Double-Graph Based Framework for Frame Semantic Parsing—0
Discourse Representation Parsing for Sentences and Documents—0
Evaluation Strategies for Computational Construction Grammars—0
Disambiguating Verbs by Collocation: Corpus Lexicography meets Natural Language Processing—0
EventWiki: A Knowledge Base of Major Events—0
Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs—0
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)—0
Exploiting Frame-Semantics and Frame-Semantic Parsing for Automatic Extraction of Typological Information from Descriptive Grammars of Natural Languages—0
A Semantic Parsing Algorithm to Solve Linear Ordering Problems—0
Frame-Semantic Parsing—0
Frame-Semantic Role Labeling with Heterogeneous Annotations—0
Bootstrapping Multilingual Semantic Parsers using Large Language Models—0
Dijkstra-WSA: A Graph-Based Approach to Word Sense Alignment—0
A Bayesian Approach to Unsupervised Semantic Role Induction—0
A Hybrid Semantic Parsing Approach for Tabular Data Analysis—0
Compositional Generalization for Natural Language Interfaces to Web APIs—0
Extracting a bilingual semantic grammar from FrameNet-annotated corpora—0
A Discriminative Graph-Based Parser for the Abstract Meaning Representation—0
Extrinsic Evaluation of Machine Translation Metrics—0
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging—0
Fast Forward Through Opportunistic Incremental Meaning Representation Construction—0
FLIN: A Flexible Natural Language Interface for Web Navigation—0
Fast semantic parsing with well-typedness guarantees—0
A Corpus of Preposition Supersenses—0
FedParsing: a Semi-Supervised Federated Learning Model on Semantic Parsing—0
Did You Mean...? Confidence-based Trade-offs in Semantic Parsing—0
Book Reviews: Ontology-Based Interpretation of Natural Language by Philipp Cimiano, Christina Unger and John McCrae—0
Show:102550
← PrevPage 8 of 25Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ARTEMIS-DAAccuracy (Test)80.8—Unverified
2SynTQA (Oracle)Test Accuracy77.5—Unverified
3TabLaPAccuracy (Test)76.6—Unverified
4SynTQA (GPT)Accuracy (Test)74.4—Unverified
5Mix SCAccuracy (Test)73.6—Unverified
6SynTQA (RF)Accuracy (Test)71.6—Unverified
7CABINETAccuracy (Test)69.1—Unverified
8NormTab+TabSQLifyAccuracy (Test)68.63—Unverified
9Chain-of-TableAccuracy (Test)67.31—Unverified
10Tab-PoTAccuracy (Test)66.78—Unverified
#ModelMetricClaimedVerifiedStatus
1RESDSQL-3B + NatSQLAccuracy84.1—Unverified
2code-davinci-002 175B (LEVER)Accuracy81.9—Unverified
3RASAT+PICARDAccuracy75.5—Unverified
4Graphix-3B + PICARDAccuracy74—Unverified
5T5-3B + PICARDAccuracy71.9—Unverified
6SADGA + GAPAccuracy70.1—Unverified
7RATSQL + GAPAccuracy69.7—Unverified
8RATSQL + Grammar-Augmented Pre-TrainingAccuracy69.6—Unverified
9RATSQL + BERTAccuracy65.6—Unverified
10Exact Set MatchingAccuracy19.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Dynamic Least-to-Most PromptingExact Match95—Unverified
2LeARExact Match90.9—Unverified
3T5-3B w/ Intermediate RepresentationsExact Match83.8—Unverified
4Hierarchical Poset DecodingExact Match69—Unverified
5Universal TransformerExact Match18.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ReaRevAccuracy76.4—Unverified
2NSM+hAccuracy74.3—Unverified
3CBR-KBQAAccuracy70—Unverified
4STAGG (Yih et al., 2016)Accuracy63.9—Unverified
5T5-11B (Raffel et al., 2020)Accuracy56.5—Unverified
#ModelMetricClaimedVerifiedStatus
1CABINETDenotation accuracy (test)89.5—Unverified
2TAPEX-Large (weak supervision)Denotation accuracy (test)89.5—Unverified
3ReasTAP-Large (weak supervision)Denotation accuracy (test)89.2—Unverified
4NL2SQL-BERTAccuracy89—Unverified
5TAPAS-Large (weak supervision)Denotation accuracy (test)83.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PhraseTransformerAccuracy90.4—Unverified
2TranxAccuracy86.2—Unverified
3ASN (Rabinovich et al., 2017)Accuracy85.3—Unverified
4ZH15 (Zhao and Huang, 2015)Accuracy84.2—Unverified
#ModelMetricClaimedVerifiedStatus
1coarse2fineAccuracy88.2—Unverified
2PhraseTransformerAccuracy87.9—Unverified
3TranxAccuracy87.7—Unverified
#ModelMetricClaimedVerifiedStatus
1PERIN + RobeCzechF192.36—Unverified
2PERINF192.24—Unverified
3HUJI-KUF158—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF180.52—Unverified
2HUJI-KUF145—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF180.23—Unverified
2HUJI-KUF152—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF194.16—Unverified
2HUJI-KUF163—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF189.83—Unverified
2HUJI-KUF162—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF192.73—Unverified
2HUJI-KUF180—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF189.19—Unverified
2HUJI-KUF154—Unverified
#ModelMetricClaimedVerifiedStatus
1TAPEX-LargeDenotation Accuracy74.5—Unverified
2TAPAS-LargeAccuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF176.4—Unverified
2HUJI-KUF173—Unverified
#ModelMetricClaimedVerifiedStatus
1PERINF181.01—Unverified
2HUJI-KUF175—Unverified
#ModelMetricClaimedVerifiedStatus
1HSPEM66.18—Unverified
#ModelMetricClaimedVerifiedStatus
1ReasonBERTRF1 Score41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1MeMCEExact40.3—Unverified