The impact of lexical and grammatical processing on generating code from natural language

Published in

Findings(2022)

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TL;DR

The paper highlights the importance of the lexical substitution component in the current natural language to code systems with a state-of-the-art architecture that relies on BERT encoder and a grammar-based decoder for which a formalization is provided.

Abstract

Considering the seq2seq architecture of Yin and Neubig (2018) for natural language to code translation, we identify four key components of importance: grammatical constraints, lexical preprocessing, input representations, and copy mechanisms. To study the impact of these components, we use a state-of-the-art architecture that relies on BERT encoder and a grammar-based decoder for which a formalization is provided. The paper highlights the importance of the lexical substitution component in the current natural language to code systems.

Authors

Benoit Crabb'e

2 papers

Nathanael Beau

1 papers

References19 items

Program Synthesis with Large Language Models

Evaluating Large Language Models Trained on Code

Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation

Measuring Coding Challenge Competence With APPS

Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data

The impact of lexical and grammatical processing on generating code from natural language

Published in

Findings(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Benoit Crabb'e

2 papers

Nathanael Beau

1 papers

The impact of lexical and grammatical processing on generating code from natural language

TL;DR

Abstract

Authors

References19 items

Program Synthesis with Large Language Models

Evaluating Large Language Models Trained on Code

Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation

Measuring Coding Challenge Competence With APPS

Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data

The impact of lexical and grammatical processing on generating code from natural language

TL;DR

Abstract

Authors

References19 items

Program Synthesis with Large Language Models

Evaluating Large Language Models Trained on Code

Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation

Measuring Coding Challenge Competence With APPS

Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

Attention is All you Need

Abstract Syntax Networks for Code Generation and Semantic Parsing

Language to Logical Form with Neural Attention

Effective Approaches to Attention-based Neural Machine Translation

Pointer Networks

The Zephyr Abstract Syntax Description Language

An efficient context-free parsing algorithm

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Copied Monolingual Data Improves Low-Resource Neural Machine Translation

A syntactic neural model for general-purpose code generation

The zephyr ab - 636 stract syntax description language A syntac - 641 tic neural model for general - purpose code genera - 642 tion

2019. BERT: pre-training

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