Deep Learning neural network models have been successfully applied to natural language processing, and are now changing radically how we interact with machines (Siri, Amazon Alexa, Google Home, Skype translator, Google Translate, or the Google search engine). These models are able to infer a continuous representation for words and sentences, instead of using hand-engineered features as in other machine learning approaches. The seminar will introduce the main deep learning models used in natural language processing, allowing the attendees to gain hands-on understanding and implementation of them in Keras.

This course is a 20 hour introduction to the main deep learning models used in text processing, covering the latest developments, including Transformers and pre-trained (multilingual) language models like GPT, BERT and XLM. It combines theoretical and practical hands-on classes. Attendants will be able to understand and implement the models in Keras.

Student profile

Addressed to professionals, researchers and students who want to understand and apply deep learning techniques to text. The practical part requires basic programming experience, a university-level course in computer science and experience in Python. Basic math skills (algebra or pre-calculus) are also needed.

Contents

Introduction to machine learning and NLP with Keras

Machine learning, Deep learning
Natural Language Processing
A sample NLP task with ML
. Sentiment analysis
. Features
. Logistic Regression
LABORATORY: Sentiment analysis with logistic regression

Multilayer Perceptron and Word Embeddings

Multiple layers ~ Deep: MLP
Backpropagation and gradients
Learning rate
More regularization
Hyperparameters
Representation learning
Word embeddings
LABORATORY: Sentiment analysis with Multilayer Perceptron

Recurrent Neural Networks,Seq2seq, Neural Machine Translation

From words to sequences: RNNs
. Language Models (sentence encoders)
. Language Generation (sentence decoders)
. Sequence to sequence models and Neural Machine Translation (I)
Better RNNs: LSTM
LABORATORY: Sentiment analysis with LSTMs

Attention, Better Machine Translation and Natural Language Inference

Re-thinking seq2seq:
. Attention and memory
. State of the art NMT with Transformers
Natural Language Inference with siamese networks
LABORATORY: Attention Model for NLI

Convolutional neural networks for text

CNNs for text
Transfer Learning
Deep learning frameworks
Last words
LABORATORY: TBA

Invited talk

To be announced

Instructors

Person 1

Eneko Agirre

Professor, member of IXA

Person 2

Gorka Azkune

Asist. prof., member of IXA

Person 3

Olatz Perez de Vinaspre

Assist. prof., member of IXA

Person 2

Ander Barrena

Postdoc researcher at IXA

Practical details

General information

Bring your own laptop (in order to do the practical side).
Part of the Language Analysis and Processing master program.
5 theoretical sessions with interleaved labs (20 hours), plus an invited talk.
Scheduled from July 1st to 3rd 2020, 9:00-13:00 14:30-18:30 (Friday ends 16:00).

Where: "Ada Lovelace", Computer science faculty, San Sebastian
(practical classes will be held in labs, split groups).
The university provides some limited information about accommodation in San Sebastian (Basque/Spanish) and the Basque Country (English).
Lunch on your own in one of the cafeterias on campus.
Teaching language: English.
Capacity: 60 attendants (First-come first-served).
Cost: 274 euros (270 for UPV/EHU members).

Registration

Registration open: now to the 20th of June 2020 (or until room is full).
Please register by email to amaia.lorenzo@ehu.eus (subject "Registration to DL4NLP" and CC e.agirre@ehu.eus).
Same for any enquiry you might have.


Prerequisite
Basic programming experience, a university-level course in computer science and experience in Python. Basic math skills (algebra or pre-calculus) are also needed.
Bring your own laptop (no need to install anything).

Previous editions