YSDA Natural Language Processing course
- This is the 2020 version. For previous year' course materials, go to this branch
- Lecture and seminar materials for each week are in ./week* folders, see README.md for materials and instructions
- YSDA homework deadlines will be listed in Anytask (read more).
- Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
- Installing libraries and troubleshooting: this thread.
week01 Word Embeddings
- Lecture: Word embeddings. Distributional semantics. Count-based (pre-neural) methods. Word2Vec: learn vectors. GloVe: count, then learn. Evaluation: intrinsic vs extrinsic. Analysis and Interpretability. Interactive lecture materials and more.
- Seminar: Playing with word and sentence embeddings
- Homework: Embedding-based machine translation system
week02 Text Classification
- Lecture: Text classification: introduction and datasets. General framework: feature extractor + classifier. Classical approaches: Naive Bayes, MaxEnt (Logistic Regression), SVM. Neural Networks: General View, Convolutional Models, Recurrent Models. Practical Tips: Data Augmentation. Analysis and Interpretability. Interactive lecture materials and more.
- Seminar: Text classification with convolutional NNs.
- Homework: Statistical & neural text classification.
week03 Language Modeling
- Lecture: Language Modeling: what does it mean? Left-to-right framework. N-gram language models. Neural Language Models: General View, Recurrent Models, Convolutional Models. Evaluation. Practical Tips: Weight Tying. Analysis and Interpretability. Interactive lecture materials and more.
- Seminar: Build a N-gram language model from scratch
- Homework: Neural LMs & smoothing in count-based models.
week04 Seq2seq and Attention
- Lecture: Seq2seq Basics: Encoder-Decoder framework, Training, Simple Models, Inference (e.g., beam search). Attention: general, score functions, models. Transformer: self-attention, masked self-attention, multi-head attention; model architecture. Subword Segmentation (BPE). Analysis and Interpretability: functions of attention heads; probing for linguistic structure. Interactive lecture materials and more.
- Seminar: Basic sequence to sequence model
- Homework: Machine translation with attention
week05 Transfer Learning
- Lecture: What is Transfer Learning? Great idea 1: From Words to Words-in-Context (CoVe, ELMo). Great idea 2: From Replacing Embeddings to Replacing Models (GPT, BERT). (A Bit of) Adaptors. Analysis and Interpretability. Interactive lecture materials and more.
week06 Domain Adaptation
- Lecture: General theory. Instance weighting. Proxy-labels methods. Feature matching methods. Distillation-like methods.
- Seminar+Homework: BERT-based NER domain adaptation
week12 Text Summarization
- Invited Lecture by Arthur Bražinskas, University of Edinburgh. Intro: different views on summarization. Extractive vs abstractive summarization, evaluation. Overview of the two main domains: news summarization and opinion summarization. Abstractive summarization: pointer-generator network and modern approaches (BertSum, BART, MeanSum, Copycat). Few-shot learning for opinion summarization.
Contributors & course staff
Course materials and teaching performed by