| Date | Topic | Readings | Discussion Leaders | Todo's |
|---|
| Jan 11 | Intro to Class | -- | Mohit | - |
| Jan 18 | Multi-Task Learning 1 | (1) Multi-task Sequence to Sequence Learning;
(2) A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks;
(3) Multi-Task Video Captioning with Video and Entailment Generation;
(4) One Model To Learn Them All;
(5) The Natural Language Decathlon: Multitask Learning as Question Answering;
| Darryl, Yichen, Mohit | - |
| Jan 25 | Multi-Task Learning 2 | (1) Deep multi-task learning with low level tasks supervised at lower layers;
(2) Soft, Layer-Specific Multi-Task Summarization with Entailment and Question Generation;
(3) When is multitask learning effective? Semantic sequence prediction under varying data conditions;
(4) Latent Multi-task Architecture Learning;
(5) Dynamic Multi-Level Multi-Task Learning for Sentence Simplification;
| Xiang, Shiyue, Han, Mohit | - |
| Feb 1 | Reinforcement Learning 1 | (1) Sequence Level Training with Recurrent Neural Networks;
(2) A Deep Reinforced Model for Abstractive Summarization;
(3) Multi-Reward Reinforced Summarization with Saliency and Entailment;
(4) Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting;
(5) Deep Reinforcement Learning for Dialogue Generation;
| Tianxiang, Yubo, Ram, Mohit | - |
| Feb 8 | Reinforcement Learning 2 and
Architecture Learning | (1) Self-critical Sequence Training for Image Captioning;
(2) Reward Augmented Maximum Likelihood for Neural Structured Prediction;
(3) Learning to Reason: End-to-End Module Networks for Visual Question Answering;
(4) Neural Architecture Search with Reinforcement Learning;
(5) Efficient Neural Architecture Search via Parameter Sharing;
| Yang, Larry, Tong, Mohit | - |
| Feb 15 | Unsupervised Learning, Pretraining, and Fine-Tuning | (1) Phrase-Based & Neural Unsupervised Machine Translation;
(2) When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?;
(3) Deep contextualized word representations (ELMo);
(4) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding;
(5) Improving Language Understanding by Generative Pre-Training;
| Yichen, Darryl, Mohit | - |
| Feb 22 | Transfer Learning and Domain-Adaptation 2 | (1) Universal Language Model Fine-tuning for Text Classification;
(2) Strong Baselines for Neural Semi-Supervised Learning under Domain Shift;
(3) Semi-Supervised Sequence Modeling with Cross-View Training;
(4) Unsupervised Domain Adaptation by Backpropagation;
(5) Learning Robust Representations by Projecting Superficial Statistics Out;
| Shiyue, Xiang, Mohit | - |
| Mar 1 | Meta-Learning | (1) Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks;
(2) Meta-Learning for Low-Resource Neural Machine Translation;
(3) Natural Language to Structured Query Generation via Meta-Learning;
(4) Learning to Reweight Examples for Robust Deep Learning;
(5) Learning Unsupervised Learning Rules;
| Yang, Tianxiang, Mohit | - |
| Mar 8 | Midterm Project Proposal Presentations | -- | All | - |
| Mar 15 | Spring Break: No Class | -- | -- | - |
| Mar 22 | Multi-View/Correlational Representation Learning | (1) Improving Vector Space Word Representations Using Multilingual Correlation;
(2) Deep Multilingual Correlation for Improved Word Embeddings;
(3) Multi-View Learning of Word Embeddings via CCA;
(4) Multi-view Recurrent Neural Acoustic Word Embeddings;
| Yubo, Larry, Mohit | Midterm Write-up Due Mar24 |
| Mar 29 | Adversarial Learning, GANs, Data Augmentation 1 | (1) Adversarial Examples for Evaluating Reading Comprehension Systems;
(2) Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models;
(3) Robust Machine Comprehension Models via Adversarial Training;
(4) Adversarial Learning for Neural Dialogue Generation;
(5) Self-Training for Jointly Learning to Ask and Answer Questions;
(6) Speaker-Follower Models for Vision-and-Language Navigation;
| Shiyue, Yichen, Mohit | - |
| Apr 5 | Adversarial Learning, GANs, Data Augmentation 2 | (1) SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient ;
(2) Improved Training of Wasserstein GANs ;
(3) Language Generation with Recurrent Generative Adversarial Networks without Pre-training ;
(4) Adversarial Feature Matching for Text Generation ;
(5) Long Text Generation via Adversarial Training with Leaked Information ;
(6) MaskGAN: Better Text Generation via Filling in the______ ;
| Xiang, Yang, Yubo, Mohit | - |
| Apr 12 | Active Learning | (1) Active Learning for Natural Language Processing (Literature Review) ;
(2) Learning how to Active Learn: A Deep Reinforcement Learning Approach ;
(3) Learning How to Actively Learn: A Deep Imitation Learning Approach ;
(4) Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study ;
(5) Learning a Policy for Opportunistic Active Learning ;
| Darryl, Tianxiang, Larry, Mohit | - |
| Apr 19 | University Holiday (No Class) | -- | -- | - |
| Apr 26 | Final Project Presentations (Last Class) | -- | All | - |
| May 6 | Final Write-Up's Due | -- | All | - |