This week, Jetic will give us a practice talk of his depth exam on Memory Network. A zoom link will be sent tomorrow morning.
Abstract: Incorporating knowledge external to the natural language query in a Natural Language Processing (NLP) task has always been computationally challenging, not only because of useful information can come in great quantitiy but also great variety. External knowledge can take the form of documents, tables, figures, or even entire databases. Traditional NLP models using RNN or Transformer based encoders despite strong performance when the input query is limited to a sentence or aparagraph, face limitations when handling these type of information. With the introduction of memory network, a type of neural architecture that allows for separate memory components for storing these external knowledge, Neural NLP models have a much better chance of utilising structured knowledge such as a knowledge graph as well as performing complex dynamics.
Tuesday, July 20th, 09:30 a.m.