2.1k citations · 2.8k across the 24 of their papers we have counts for
24 papers
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis
Kensen Shi, Joey Hong, Yinlin Deng +3
When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neur…
Natural Language to Code Generation in Interactive Data Science Notebooks
Pengcheng Yin, Wen-Ding Li, Kefan Xiao +9
Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. T…
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
Ningyi Liao, Dingheng Mo, Siqiang Luo +2
Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in sol…
Compositional Generalization and Decomposition in Neural Program Synthesis
Kensen Shi, Joey Hong, Manzil Zaheer +2
When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neur…
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin +64
Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of…
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data
Shuyan Zhou, Li Zhang, Yue Yang +4
Procedures are inherently hierarchical. To "make videos", one may need to "purchase a camera", which in turn may require one to "set a budget". While such hierarchical knowledge is…
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Tianbao Xie, Chen Henry Wu, Peng Shi +20
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.…
Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language
Shuyan Zhou, Pengcheng Yin, Graham Neubig
When humans conceive how to perform a particular task, they do so hierarchically: splitting higher-level tasks into smaller sub-tasks. However, in the literature on natural languag…
Learning Structural Edits via Incremental Tree Transformations
Ziyu Yao, Frank F. Xu, Pengcheng Yin +2
While most neural generative models generate outputs in a single pass, the human creative process is usually one of iterative building and refinement. Recent work has proposed mode…
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Pengcheng Yin, Graham Neubig, Wen-tau Yih +1
Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-f…
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
Frank F. Xu, Zhengbao Jiang, Pengcheng Yin +2
Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that dev…
Merging Weak and Active Supervision for Semantic Parsing
Ansong Ni, Pengcheng Yin, Graham Neubig
A semantic parser maps natural language commands (NLs) from the users to executable meaning representations (MRs), which are later executed in certain environment to obtain user-de…
DIRE: A Neural Approach to Decompiled Identifier Naming
Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz +4
The decompiler is one of the most common tools for examining binaries without corresponding source code. It transforms binaries into high-level code, reversing the compilation proc…
Improving Open Information Extraction via Iterative Rank-Aware Learning
Zhengbao Jiang, Pengcheng Yin, Graham Neubig
Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. A key step in open IE is confidence modeling, ranking the extract…
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
Haohan Wang, Xindi Wu, Zeyi Huang +1
We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN). We first notice CNN's ability i…
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…
Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML
Xuezhe Ma, Pengcheng Yin, Jingzhou Liu +2
Reward augmented maximum likelihood (RAML), a simple and effective learning framework to directly optimize towards the reward function in structured prediction tasks, has led to a…
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg +22
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits l…
Neural Enquirer: Learning to Query Tables with Natural Language
Pengcheng Yin, Zhengdong Lu, Hang Li +1
We proposed Neural Enquirer as a neural network architecture to execute a natural language (NL) query on a knowledge-base (KB) for answers. Basically, Neural Enquirer finds the dis…