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Learning to select data for transfer learning with Bayesian Optimization
Sebastian Ruder, Barbara Plank
Domain similarity measures can be used to gauge adaptability and select suitable data for transfer learning, but existing approaches define ad hoc measures that are deemed suitable…
An Overview of Multi-Task Learning in Deep Neural Networks
Sebastian Ruder
Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discove…
A Survey Of Cross-lingual Word Embedding Models
Sebastian Ruder, Ivan Vulić, Anders Søgaard
Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural l…
Latent Multi-task Architecture Learning
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein +1
Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormou…
Data Selection Strategies for Multi-Domain Sentiment Analysis
Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing a…
Knowledge Adaptation: Teaching to Adapt
Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Domain adaptation is crucial in many real-world applications where the distribution of the training data differs from the distribution of the test data. Previous Deep Learning-base…