There are 22 repositories under semantic-parsing topic.
OpenAgents: An Open Platform for Language Agents in the Wild
[EMNLP 2022] Unifying and multi-tasking structured knowledge grounding with language models
Neural Symbolic Machines is a framework to integrate neural networks and symbolic representations using reinforcement learning, with applications in program synthesis and semantic parsing.
Multiple paper open-source codes of the Microsoft Research Asia DKI group
ICLR 2022 Paper, SOTA Table Pre-training Model, TAPEX: Table Pre-training via Learning a Neural SQL Executor
A list of recent papers about Meta / few-shot learning methods applied in NLP areas.
Provide Semantic Parsing solutions and Natural Language Inferences for multiple languages following the idea of the syntax-semantics interface.
SoTA Abstract Meaning Representation (AMR) parsing with word-node alignments in Pytorch. Includes checkpoints and other tools such as statistical significance Smatch.
Translating natural language questions to a structured query language
Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179
[ACL 2021] This is the project containing source codes and pre-trained models about ACL2021 Long Paper ``LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations".
A dataset of complex questions on semi-structured Wikipedia tables
SPRING is a seq2seq model for Text-to-AMR and AMR-to-Text (AAAI2021).
GAP-text2SQL: Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Distributional Generalization in NLP. A roadmap.
GenieNLP: A versatile codebase for any NLP task
SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases (AAAI 2020)
Frame Semantic Parser based on T5 and FrameNet
PyTorch port of the paper "Language to Logical Form with Neural Attention"
Organized inventory of research using the Abstract Meaning Representation
This repo in the implementation of EMNLP'21 paper "SPARQLing Database Queries from Intermediate Question Decompositions" by Irina Saparina, Anton Osokin