This is build for group work, may include all the information and materials used in our course, including but not limited to:

• paper list(NLP focused)
• paper summary
• presentation slides
• final work

• Yamei
• Jason
• Shuang
• Mohammad

• Understanding Convolutional Neural Networks for Text Classification
• A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
• Convolutional Neural Networks for sentence classification
• 2017 Very Deep Convolutional Networks for Text Classification
• 2016 Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts
• 2017 CNN for situations understanding based on sentiment analysis of twitter data
• Blog: Best Practices for Text Classification with Deep Learning by Jason Brownlee on October 23, 20
• A CNN-BiLSTM Model for Document-Level Sentiment Analysis
• Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis
• Sentiment analysis of movie reviews based on cnn-blstm.
• Multi-Channel Lexicon Integrated CNN-BiLSTM Models for Sentiment Analysis.
• Analyzing and Interpreting Convolutional Neural Networks in NLP
• Interpreting Neural Networks to Improve Politeness Comprehension
• ACTIVIS: Visual Exploration of Industry-Scale Deep Neural Network Models
• Visualizing and Understanding Neural Models in NLP
• Attention Visualization of Gated Convolutional Neural Networks with Self Attention in Sentiment Analysis
• Learning Sentimental Weights of Mixed-gram Terms for Classification and Visualization
• Explaining Predictions of Non-Linear Classifiers in NLP
• Interpreting Self-Attention Weights
• Interpreting Neural Networks to Improve Politeness Comprehension
• Understanding Neural Networks through Representation Erasure
• Explaining Recurrent Neural Network Predictions in Sentiment Analysis
• "What is relevant in a text document?": An interpretable machine learning approach

• Importance of Self-Attention for Sentiment Analysis
• Adversarial Attack on Sentiment Classification
• Sentiment analysis is not solved! Assessing and probing sentiment classification
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• Sentiment Classification Using Document Embeddings Trained with Cosine Similarity
• Fine-grained Sentiment Classification using BERT
• Unsupervised Data Augmentation
• A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification
• BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
• Mazajak: An Online Arabic Sentiment Analyser

• Detecting Political Bias in News Articles Using Headline Attention
• What does BERT look at? An Analysis of BERT’s Attention
• Analyzing the Structure of Attention in a Transformer Language Model
• Exploiting Attention to Reveal Shortcomings in Memory Models
• Extracting Syntactic Trees from Transformer Encoder Self-Attentions

• Interpreting Neural Networks with Nearest Neighbors
• Interpretable Neural Architectures for Attributing an Ad’s Performance to its Writing Style
• Learning Explanations from Language Data
• Open Sesame: Getting Inside BERT’s Linguistic Knowledge
• Interpreting deep models for text analysis via optimization and regularization methods
• Visualisation and ‘diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure
• Representation of Linguistic Form and Function in Recurrent Neural Networks
• Analyzing Linguistic Knowledge in Sequential Model of Sentence
• Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
• Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
• Interpreting Recurrent and Attention-Based Neural Models: A Case Study on Natural Language Inference
• Are BLEU and Meaning Representation in Opposition?
• Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context

• Modeling Paths for Explainable Knowledge Base Completion

• GNNExplainer: Generating Explanations for Graph Neural Networks
• Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

• Analyzing Learned Representations of a Deep ASR Performance Prediction Model
• Evaluating Textual Representations through Image Generation
• LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation
• Interpretable Textual Neuron Representations for NLP
• Interpretable Word Embedding Contextualization
• Portable, layer-wise task performance monitoring for NLP models
• Debugging Sequence-to-Sequence Models with SEQ2SEQ-VIS
• Multi-Granular Text Encoding for Self-Explaining Categorization
• Evaluating Recurrent Neural Network Explanations
• Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
• Understanding Neural Networks Through Deep Visualization.
• Visualizing and Measuring the Geometry of BERT. 
• Interpreting CNNs via Decision Trees
• Visual Interpretability for Deep Learning: a Survey
• Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
• Inverting Visual Representations with Convolutional Networks
• Towards Better Analysis of Deep Convolutional Neural Networks
• Interpretable Convolutional Neural Networks
• Visualizing and Understanding Convolutional Networks