Australia Deep Learning NLP Group
Organised by The University Of Sydney, The University Of Western Australia
1.30pm – 4.30pm (UTC/GMT+8, AWST) on Monday, 05 December 2022
When a human speaks to a machine how does the latter elicit meaning from the communication? This is an important AI task as it enables the machine to construct a sensible answer or perform a useful action for the human. Meaning is represented at the sentence level, identification of which is known as intent detection, and at the word level, a labelling task called slot filling. This dual level joint task requires innovative thinking about natural language and deep learning network design and as a result many approaches have been tried.
In this tutorial we will discuss how the joint task is set up and introduce Spoken Language Processing (NLP) and Deep Learning basics. We will cover the datasets, experiments and metrics used in the field. We will describe how the machine uses the latest NLP and Deep Learning techniques to address the task, including recurrent and non-recurrent (attention based Transformer) networks and pre-trained models (e.g. BERT). We will then look in detail at a network that allows the two levels of the task to explicitly interact to boost performance. We will do a code walk through of a Python notebook for this model and attendees will have an opportunity to do some light coding tasks on this model to further their understanding.
Time | Topic |
---|---|
13:30 - 14:00 | Introduction to the NLP and SLU |
14:00 - 14:30 | Joint SLU Approaches |
14:30 - 14:45 | QnA and Break |
14:45 - 15:00 | Hands-on Exercise (Joint BERT) |
15:00 - 15:15 | SLU Evaluation |
15:15 - 16:00 | Hands-on Exercise (Datasets, Metrics, Experiments) |
16:00 - 16:15 | Future Direction |
16:15 - 16:30 | QnA |