Semantic Parsing

358 papers with code • 20 benchmarks • 40 datasets

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


Use these libraries to find Semantic Parsing models and implementations

Most implemented papers

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

huggingface/transformers arXiv 2019

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).

Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold

Noahs-ARK/open-sesame 29 Jun 2017

We present a new, efficient frame-semantic parser that labels semantic arguments to FrameNet predicates.

A Syntactic Neural Model for General-Purpose Code Generation

pcyin/NL2code ACL 2017

We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python.

StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

pcyin/tranX ACL 2018

Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures.

Language to Logical Form with Neural Attention

donglixp/lang2logic ACL 2016

Semantic parsing aims at mapping natural language to machine interpretable meaning representations.

The Natural Language Decathlon: Multitask Learning as Question Answering

salesforce/decaNLP ICLR 2019

Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.

A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization

naver/sqlova 4 Feb 2019

We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset.

SParC: Cross-Domain Semantic Parsing in Context

ryanzhumich/editsql ACL 2019

The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.

TAPAS: Weakly Supervised Table Parsing via Pre-training

google-research/tapas ACL 2020

In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms.