2.1k citations · 2.8k across the 24 of their papers we have counts for
6 papers · 1 filter
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
Pengcheng Yin, Graham Neubig
We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs). TRANX uses a transition system bas…
Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis +2
We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient informat…
Retrieval-Based Neural Code Generation
Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru +3
In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to gene…
A Tree-based Decoder for Neural Machine Translation
Xinyi Wang, Hieu Pham, Pengcheng Yin +1
Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations. Most existing work utilizes…
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
Pengcheng Yin, Chunting Zhou, Junxian He +1
Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utter…
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow
Pengcheng Yin, Bowen Deng, Edgar Chen +2
For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise. However, creating these models require par…