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Recurrent Neural Network

• Generating Informative Conversational Response using Recurrent Knowledge-Interaction and Knowledge-Copy

  Xiexiong Lin, Weiyu Jian, Jianshan He, Taifeng Wang and Wei Chu

• MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

  Jie Lei, Liwei Wang, Yelong Shen, Dong Yu, Tamara Berg and Mohit Bansal

• Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension

  Hongyu Gong, Yelong Shen, Dian Yu, Jianshu Chen and Dong Yu

• Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment

  Forrest Davis and Marten van Schijndel

• Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction

  Shaowei Chen, Jie Liu, Yu Wang, Wenzheng Zhang and Ziming Chi

Autoencoder

• Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics

  Guy Emerson

• Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder

  Daya Guo, Duyu Tang, Nan Duan, Jian Yin, Daxin Jiang and Ming Zhou

• Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders

  Zixia Jia, Youmi Ma, Jiong Cai and Kewei Tu

• Autoencoding Keyword Correlation Graph for Document Clustering

  Billy Chiu, Sunil Kumar Sahu, Derek Thomas, Neha Sengupta and Mohammady Mahdy

• Crossing Variational Autoencoders for Answer Retrieval

  Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng and Meng Jiang

• Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder

  Fan Zhou, Shengming Zhang and Yi Yang

• SCAR: Sentence Compression using Autoencoders for Reconstruction

  Chanakya Malireddy, Tirth Maniar and Manish Shrivastava

LSTM

• Inducing Grammar from Long Short-Term Memory Networks by Shapley Decomposition

  Yuhui Zhang and Allen Nie

Sequence

• A Study of Non-autoregressive Model for Sequence Generation

  Yi Ren, Jinglin Liu, Xu Tan, Zhou Zhao, Sheng Zhao and Tie-Yan Liu

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

  Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov and Luke Zettlemoyer

• Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation

  Kun Li, Chengbo Chen, Xiaojun Quan, Qing Ling and Yan Song

• DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking

  Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi, Siddharth Varia, Kriste Krstovski, Mona Diab and Smaranda Muresan

• Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks

  Fynn Schröder and Chris Biemann

• Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation

  Junliang Guo, Linli Xu and Enhong Chen

• Location Attention for Extrapolation to Longer Sequences

  Yann Dubois, Gautier Dagan, Dieuwke Hupkes and Elia Bruni

• NAT: Noise-Aware Training for Robust Neural Sequence Labeling

  Marcin Namysl, Sven Behnke and Joachim Köhler

• SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling

  Luoxin Chen, Weitong Ruan, Xinyue Liu and Jianhua Lu

• Structure-Level Knowledge Distillation For Multilingual Sequence Labeling

  Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang and Kewei Tu

• Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing

  Daniel Fernández-González and Carlos Gómez-Rodríguez

• Low Resource Sequence Tagging using Sentence Reconstruction

  Tal Perl, Sriram Chaudhury and Raja Giryes

• Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition

  Takuma Kato, Kaori Abe, Hiroki Ouchi, Shumpei Miyawaki, Jun Suzuki and Kentaro Inui

Data augmentation

• AdvAug: Robust Adversarial Augmentation for Neural Machine Translation

  Yong Cheng, Lu Jiang, Wolfgang Macherey and Jacob Eisenstein

• Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation

  Kun Li, Chengbo Chen, Xiaojun Quan, Qing Ling and Yan Song

• Good-Enough Compositional Data Augmentation

  Jacob Andreas

  由于语言任务中的某些模式具有通用性，为了让神经网络学习到这些通用性，从而提出这种增强方法，具体方法：

1. 分析数据集中的语言模式，即在同样的语言环境中出现的不同词句，这些不同字句就是需要被学习到的通用性，下面是一对例子。
   1. She picks the wug up in Fresno.
   2. She puts the wug down in Tempe.
2. 在这个例子中，粗体部分代表着同样的语言环境，则斜体部分则为需要学习到的通用性，在网络受到1的句子的时候，也需要具备推导出2中斜体部分内容的能力。

• Review-based Question Generation with Adaptive Instance Transfer and Augmentation

  Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam and Luo Si

• Logic-Guided Data Augmentation and Regularization for Consistent Question Answering

  Akari Asai and Hannaneh Hajishirzi

• Parallel Data Augmentation for Formality Style Transfer

  Yi Zhang, Tao Ge and Xu SUN

• Syntactic Data Augmentation Increases Robustness to Inference Heuristics

  Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler and Tal Linzen

• Noise-Based Augmentation Techniques for Emotion Datasets: What do we Recommend?

  Mimansa Jaiswal and Emily Mower Provost