Semantic Parsing Edit Task
Natural Language Processing
About
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
Benchmarks
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Subtasks
Datasets
Greatest papers with code
TAPAS: Weakly Supervised Table Parsing via Pre-training
ACL 2020
• huggingface/transformers
• 
In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms.
Ranked #1 on
Semantic Parsing
on SQA
Learning to Generalize from Sparse and Underspecified Rewards
• google-research/google-research
• 
The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy.
SPARQL query generation for complex question answering with BERT and BiLSTM-based model
In this paper we describe question answering system for answering of complex questions over Wikidata knowledge base.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
CVPR 2017
• charlesq34/pointnet
•
Point cloud is an important type of geometric data structure.
Ranked #2 on
Scene Segmentation
on ScanNet
3D CLASSIFICATION 3D PART SEGMENTATION 3D POINT CLOUD CLASSIFICATION 3D SEMANTIC SEGMENTATION OBJECT CLASSIFICATION SCENE SEGMENTATION SEMANTIC PARSING SKELETON BASED ACTION RECOGNITION
The Natural Language Decathlon: Multitask Learning as Question Answering
ICLR 2019
• salesforce/decaNLP
•
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
Ranked #2 on
Natural Language Inference
on MultiNLI
(Accuracy metric)
DOMAIN ADAPTATION MACHINE TRANSLATION NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING RELATION EXTRACTION SEMANTIC PARSING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING
SLING: A framework for frame semantic parsing
We describe SLING, a framework for parsing natural language into semantic frames.
Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations.
Compositional Semantic Parsing on Semi-Structured Tables
Two important aspects of semantic parsing for question answering are the breadth of the knowledge source and the depth of logical compositionality.
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
• naver/sqlova
•
We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset.
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
ACL 2020
• facebookresearch/tabert
•
Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks.
Ranked #1 on
Semantic Parsing
on WikiTableQuestions