Introduction: Learning word and sentence representations | Further reading
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326, 2015.
Alexis Conneau and Douwe Kiela. Senteval: An evaluation toolkit for universal sentence representations. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018), 2018.
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes. Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364, 2017.
Seminar: Dependency-based word embeddings, specialisation and retro-fitting
Seminar: Dependency-based word embeddings, specialisation and retro-fitting | Abstract
In this session we will discuss three recent papers on dependency-based word embeddings, specialisation and retro-fitting.
Seminar: Contextualised representations and modelling ambiguity | Discussion
In this session we will discuss the following papers:
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee and Luke Zettlemoyer 2018. Deep contextualized word representations. In Proceedings of NAACL 2018, New Orleans, Louisiana.
Verna Dankers and Ekaterina Shutova.
Multitask learning | Abstract
In this session we will introduce the idea of multitask learning and the architectures proposed for it. We will then discuss the language processing tasks that can benefit each other through information sharing.