Lecture 21 (26/05/2023, 4.5 hours): seq2seq, Machine Translation

Foundations of sequence-to-sequence models and their use within Huggingface.

Introduction to machine translation (MT) and history of MT. Overview of statistical MT. Beam search for decoding. Introduction to neural machine translation: the encoder-decoder neural architecture. The BLEU evaluation score. Performances and recent improvements. Neural MT: the encoder-decoder architecture; Attention in NMT.

Lecture 20 (22/05/2023, 2.5 hours): More on semantic role labeling; Semantic Parsing

More on Semantic Role Labeling. Semantic Parsing: task, motivation and applications, Abstract Meaning Representation (AMR) and BabelNet Meaning Representation (BMR), Natural Language Generation from semantic parses

Lecture 19 (19/05/2023, 4 hours): Semantic Role Labeling

Semantic roles. Frame resources: PropBank, FrameNet, VerbAtlas. Semantic Role Labeling (SRL). Multilingual SRL. Cross-inventory approaches to SRL. Topics for thesis or excellence path.

Lecture 18 (15/05/2023, 2.5 hours): Overview of NLP libraries and tools; HW 3 assignment

Overview of NLP libraries: Hugginface Transformers, datasets and eval. FairSeq, Lightning Transformer, Sentence Transformers, Classy. PyTorch Lightning. Assignment of homework 3: Relation Extraction.

Lecture 17 (12/05/2023, 4.5 hours): More on sense embeddings; Word Sense Disambiguation

Word Sense Disambiguation (WSD): introduction to the task. Purely data-driven, and neuro-symbolic approaches. WSD cast as sense comprehension. Issues. Semantic Role Labeling: introduction to the task. Inventories. Neural approaches. Issues.

Lecture 16 (05/05/2023, 4.5 hours): Homework 2 assignment on Word Sense Disambiguation; sense embeddings

Assignment of homework2: Word Sense Disambiguation. Introduction to Word Sense Disambiguation. First introduction to explicit and latent sense embeddings. SensEmbed.