UoW: Multi-task Learning Gaussian Process for Semantic Textual Similarity

Miguel Angel Rios Gaona

Abstract:

We report results obtained by the UoW
method in SemEval-2014’s Task 10 – Multilingual Semantic Textual Similarity. We
propose to model Semantic Textual Similarity in the context of Multi-task Learning
in order to deal with inherent challenges of
the task such as unbalanced performance
across domains and the lack of training
data for some domains (i.e. unknown
domains). We show that the Multi-task
Learning approach outperforms previous
work on the 2012 dataset, achieves a robust performance on the 2013 dataset and
competitive results on the 2014 dataset.
We highlight the importance of the challenge of unknown domains, as it affects
overall performance substantially.