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
Object-oriented Neural Programming (OONP) for Document Understanding0
On Codex Prompt Engineering for OCL Generation: An Empirical Study0
One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets0
On graph-based reentrancy-free semantic parsing0
Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue0
On maximum spanning DAG algorithms for semantic DAG parsing0
On Robustness of Neural Semantic Parsers0
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex0
On the Compositional Generalization Gap of In-Context Learning0
On The Ingredients of an Effective Zero-shot Semantic Parser0
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics0
Open-Domain Semantic Parsing with Boxer0
OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data0
OPERA:Operation-Pivoted Discrete Reasoning over Text0
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing0
Paraphrase for Open Question Answering: New Dataset and Methods0
Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing0
Paraphrasing Revisited with Neural Machine Translation0
PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View Synthesis0
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning0
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning0
Parsing Software Requirements with an Ontology-based Semantic Role Labeler0
Pattern Learning for Relation Extraction with a Hierarchical Topic Model0
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction0
Person image generation with semantic attention network for person re-identification0
Philosophers are Mortal: Inferring the Truth of Unseen Facts0
Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation0
Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations0
Positional Attention-based Frame Identification with BERT: A Deep Learning Approach to Target Disambiguation and Semantic Frame Selection0
Practical Semantic Parsing for Spoken Language Understanding0
Predicate Matrix: extending SemLink through WordNet mappings0
Predicting Coreference in Abstract Meaning Representations0
Predicting generalization performance with correctness discriminators0
Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP0
Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time0
Priberam: A Turbo Semantic Parser with Second Order Features0
Probabilistic Dialogue Models with Prior Domain Knowledge0
Probabilistic modeling of the syntax semantic interface using probabilistic context free grammars, Experiments with FrameNet (Mod\'elisation probabiliste de l'interface syntaxe s\'emantique \`a l'aide de grammaires hors contexte probabilistes Exp\'eriences avec FrameNet) [in French]0
Probabilistic Models for Learning a Semantic Parser Lexicon0
Probabilistic Type Theory for Incremental Dialogue Processing0
How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering0
Proceedings of the ACL 2014 Workshop on Semantic Parsing0
Proceedings of the IWCS Shared Task on Semantic Parsing0
Project Notes on building a conversational parser on top of a text parser: Towards a causal language tagger for spoken Chinese0
QAF: Frame Semantics-based Question Interpretation0
QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships0
QUEST: A Natural Language Interface to Relational Databases0
Question Answering over Freebase with Multi-Column Convolutional Neural Networks0
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering0
Question Answering over Knowledge Base using Factual Memory Networks0
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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