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Natural Code Reading List

This repository is for some papers I have collected for natural language processing and programming language analysis. We are trying to build the state-of-the-art on bridging the gap between natural language and programming language.

Targeted conferences: ICML, NeurIPS, ICLR, ICSE, FSE, ASE, AAAI, IJCAI, KDD, WWW, SIGIR

A Big Picture

Image of Yaktocat

1. Fundamental theory

Deep Learning

  • LSTM
  • CNN
  • Seq2Seq Framework
  • Attention Mechanism
  • Beam Search

GAN

  • SeqGAN - SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
  • MaliGAN - Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
  • RankGAN - Adversarial ranking for language generation
  • LeakGAN - Long Text Generation via Adversarial Training with Leaked Information
  • TextGAN - Adversarial Feature Matching for Text Generation
  • GSGAN - GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution

Reinforcement Learning

  • Deep Reinforcement Learning For Sequence to Sequence Models
  • AN ACTOR-CRITIC ALGORITHM for sequence prediction
  • Actor-Critic Sequence Training for Image Captioning
  • Maximum Entropy Inverse Reinforcement Learning
  • Towards Diverse Text Generation with Inverse Reinforcement Learning
  • Deep Reinforcement Learning-based Image Captioning with Embedding Reward
  • Reinforcement Learning and Control as Probabilistic Inference-Tutorial and Review

Multi-Agent Reinforcement Learning

  • TODO

Transfer Learning/Meta-Learning

  • Model-Agnostic Meta-Learning

  • A spect-augmented Adversarial Networks for Domain Adaptation

  • Natural Language to Structured Query Generation via Meta-Learning

  • Learning a Prior over Intent via Meta-Inverse Reinforcement Learning

Graph Neural Network

Non-Autoregressive

2. Text Summarization

  • A Neural Attention Model for Sentence Summarization
  • A Deep Reinforced Model for Abstractive Summarization

3. (Visual) Q&A and Dialog

Dialog Generation

  • A Hierarchical Latent Structure for Variational Conversation Modeling
  • A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
  • Improving Variational Encoder-Decoders in Dialogue Generation
  • DialogWAE- Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
  • Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

QA

  • Cross-Dataset Adaptation for Visual Question Answering
  • Joint Image Captioning and Question Answering
  • Learning Answer Embeddings for Visual Question Answering
  • Question Answering through Transfer Learning from Large Fine-grained Supervision Data

VQA (Visual Question Answering)

  • Visual Question Answering- A Survey of Methods and Datasets
  • Visual Question Answering as a Meta Learning Task

Visual Dialog

  • Visual Dialog
  • Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
  • Are You Talking to Me-Reasoned Visual Dialog Generation through Adversarial Learning
  • Two can play this Game- Visual Dialog with Discriminative Question Generation
  • Zero-Shot Dialog Generation with Cross-Domain Latent Actions
  • Adversarial Learning of Task-Oriented Neural Dialog Models
  • Best of Both Worlds- Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

4. Program Language Processing

Survey

  • A Survey of Machine Learning for Big Code and Naturalness

Code Representation

  • LEARNING TO REPRESENT PROGRAMS WITH GRAPHS
  • A General Path-Based Representation for Predicting Program Properties
  • Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code

Text2Code & Code2Text

  • A Convolutional Attention Network for Extreme Summarization of Source Code
  • AutoComment- Mining Question and Answer Sites for Automatic Comment Generation
  • Automatic Comment Generation using a Neural
  • Convolutional Neural Networks over Tree Structures for Programming Language Processing
  • Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code
  • Exploiting Tree Structures for Classifying Programs
  • Learning to Generate Pseudo-code from Source Code using Statistical Machine Translation
  • Natural Language Models for Predicting Programming Comments
  • SEQ2SQL/ GENERATING STRUCTURED QUERIES
  • Summarizing Source Code using a Neural Attention Model
  • Sequence to Sequence Learning with neural networks
  • Tree-to-Sequence Attentional Neural Machine Translation

Program Synthesis

  • Abstract Syntax Networks for Code Generation and Semantic Parsing
  • Reinforcement Learning Neural Turing Machines

API

  • Deep API Learning
  • Exploring API Embedding for API Usages and Applications
  • DeepAM- Migrate APIs with Multi-modal Sequence to Sequence Learning

5. Semantic Parsing

Code generation

  • A Retrieve-and-Edit Framework for Predicting Structured Outputs, Hashimoto et al., NeurIPS, 2018
  • A Grammar-Based Structural CNN Decoder for Code Generation, Sun et al., AAAI, 2019
  • Program Synthesis and Semantic Parsing with Learned Code Idioms, Shin et al., NeurIPS 2019

SQL generation

  • SParC Cross-Domain Semantic Parsing in Context, Yu et al., ACL 2019
  • SyntaxSQLNet Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task, Yu et al., ACL 2019
  • Grammar-based Neural Text-to-SQL Generation, Lin et al., arXiv 2019
  • Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation, Guo et al., ACL 2019
  • Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions, Zhang et al., EMNLP 2019
  • Robust Text-to-SQL Generation with Execution-Guided Decoding, Wang et al., arXiv 2018
  • Content Enhanced BERT-based Text-to-SQL Generation, Guo et al., arXiv 2019

Structured generation

  • Non-autoregressive Neural Machine Translation, Gu et al., ICLR 2018
  • Tree-to-tree Neural Networks for Program Translation, Chen et al., NIPS 2018
  • Convolutional neural networks over tree structures for programming language processing, Sun et al., AAAI 2019

Others

  • DeepFix- Fixing Common C Language Errors by Deep Learning
  • Deep Reinforcement Learning for Programming Language Correction
  • Neural Code Completion
  • pix2code- Generating Code from a Graphical User Interface Screenshot

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A reading list on natural language processing and programming analysis.

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