Disputation: Farhad Nooralahzadeh

Doctoral candidate Farhad Nooralahzadeh at the Department of Informatics, Faculty of Mathematics and Natural Sciences, is defending the thesis Low-Resource Adaptation of Neural NLP Models for the degree of Philosophiae Doctor.

Image may contain: Tie, White-collar worker, Chin, Forehead, Tie.

The PhD defence and trial lecture are fully digital and streamed using Zoom. The host of the session will moderate the technicalities while the chair of the defence will moderate the disputation.

 

Ex auditorio questions: the chair of the defence will invite the audience to ask ex auditorio questions. This can be requested by clicking 'Participants -> Raise hand'. 

 

Trial lecture

 

"Natural Language Inference: Datasets, Methods, Current Challenges and What's Next"

 

Main research findings

Real-world applications of natural language processing (NLP) are challenging. NLP models rely heavily on supervised machine learning and require large amounts of annotated data. These resources are often based on language data available in large quantities, such as English newswire. However, in real-world applications of NLP, the textual resources vary across several dimensions, such as language, dialect, topic, and genre. It is challenging to find annotated data of sufficient amount and quality.


The objective of this thesis is to investigate methods for dealing with such low-resource scenarios in information extraction and natural language understanding. To this end, we study distant supervision and sequential transfer learning in various low-resource settings. We develop and adapt neural NLP models to explore a number of research questions concerning NLP tasks with minimal or no training data.

 

 

Contact information to Department: Pernille Adine Nordby 

Published Oct. 16, 2020 10:31 AM - Last modified Oct. 28, 2020 12:26 PM