Learning with limited labelled data in NLP: multi-task learning and beyond

Abstract:

When labelled training data for certain NLP tasks or languages is not readily available, different approaches exist to leverage other resources for the training of machine learning models. Those are commonly either instances from a related task or unlabelled data. An approach that has been found to work particularly well when only limited training data is available is multi-task learning. There, a model learns from examples of multiple related tasks at the same time by sharing hidden layers between tasks, and can therefore benefit from a larger overall number of training instances and extend the models' generalisation performance. In the related paradigm of semi-supervised learning, unlabelled data as well as labelled data for related tasks can be easily utilised by transferring labels from labelled instances to unlabelled ones in order to essentially extend the training dataset.

In this talk, I will present my recent and ongoing work in the space of learning with limited labelled data in NLP, including our NAACL 2018 paper 'Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces’ [1].

[1] https://t.co/A5jHhFWrdw

Bio:

http://isabelleaugenstein.github.io/index.html