Here is the detailed list of subjects from lecture 13 that we have presented in class and that will be matter of evaluation at the finals.
Lecture 13: Question Answering
Content: Question answering (QA) and factoid questions. Text-based QA, information retrieval and reading comprehension. Answer span extraction using contextual embeddings: start and end vectors, fine-tuning loss, and negative examples. Answer span extraction using RNN and attention: Stanford attentive reader; bilinear product and attention. Practical issues. Datasets, evaluation measures, and leaderboard. Knowledge-based QA; entity linking.
References: For answer span extraction using contextual embeddings, see the slides from the lecture. For Stanford attentive reader, see Eisenstein, section 17.5.2. Use lecture slides for datasets, evaluation measures, leaderboard knowledge-based QA, and entity linking.