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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

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Showing 601–650 of 1202 papers

TitleStatusHype
Semantic Frame Identification with Distributed Word Representations—0
Semantic Frame Parsing for Information Extraction : the CALOR corpus—0
Semantic Frames and Visual Scenes: Learning Semantic Role Inventories from Image and Video Descriptions—0
Semantic Framework for Comparison Structures in Natural Language—0
Semantic Graph Parsing with Recurrent Neural Network DAG Grammars—0
Semantic Kernels for Semantic Parsing—0
Semantic Operator Prediction and Applications—0
Semantic Parsing as Machine Translation—0
Semantic Parsing for Complex Data Retrieval: Targeting Query Plans vs. SQL for No-Code Access to Relational Databases—0
Semantic Parsing for English as a Second Language—0
Semantic Parsing for Planning Goals as Constrained Combinatorial Contextual Bandits—0
Semantic Parsing for Single-Relation Question Answering—0
Semantic Parsing for Task Oriented Dialog using Hierarchical Representations—0
Semantic Parsing for Technical Support Questions—0
Semantic Parsing for Text to 3D Scene Generation—0
Semantic Parsing for Textual Entailment—0
Semantic Parsing Freebase: Towards Open-domain Semantic Parsing—0
Semantic Parsing in Limited Resource Conditions—0
Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder—0
Semantic Parsing Natural Language into Relational Algebra—0
Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention—0
Semantic Parsing of Ambiguous Input through Paraphrasing and Verification—0
Semantic Parsing of Brief and Multi-Intent Natural Language Utterances—0
Semantic Parsing of Colonoscopy Videos with Multi-Label Temporal Networks—0
Semantic Parsing of Disfluent Speech—0
Semantic Parsing of Interpage Relations—0
Semantic Parsing of Mathematics by Context-based Learning from Aligned Corpora and Theorem Proving—0
Semantic Parsing of Pre-university Math Problems—0
Semantic parsing of speech using grammars learned with weak supervision—0
Semantic Parsing of Tamil Sentences—0
Semantic Parsing on Freebase from Question-Answer Pairs—0
Semantic Parsing: Syntactic assurance to target sentence using LSTM Encoder CFG-Decoder—0
Semantic Parsing to Manipulate Relational Database For a Management System—0
Semantic Parsing to Probabilistic Programs for Situated Question Answering—0
Semantic Parsing Using Content and Context: A Case Study from Requirements Elicitation—0
Semantic Parsing using Distributional Semantics and Probabilistic Logic—0
Semantic Parsing via l_0-norm-based Alignment—0
Semantic Parsing via Paraphrasing—0
Semantic Parsing with Bayesian Tree Transducers—0
Semantic Parsing with Combinatory Categorial Grammars—0
Semantic parsing with fuzzy meaning representations—0
Semantic Parsing with Relaxed Hybrid Trees—0
Semantic Parsing with Semi-Supervised Sequential Autoencoders—0
Semantic Parsing with Syntax- and Table-Aware SQL Generation—0
Semantic role labeling tools for biomedical question answering: a study of selected tools on the BioASQ datasets—0
Semantics as a Foreign Language—0
Semantic Parsing for Question Answering over Knowledge Graphs—0
SemEval-2014 Task 6: Supervised Semantic Parsing of Robotic Spatial Commands—0
SemEval-2017 Task 11: End-User Development using Natural Language—0
SemEval 2018 Task 6: Parsing Time Normalizations—0
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All datasetsWikiTableQuestionsspiderCFQWebQuestionsSPWikiSQLATISGeoPTG (czech, MRP 2020)AMR (chinese, MRP 2020)AMR (english, MRP 2020)DRG (english, MRP 2020)DRG (german, MRP 2020)

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