Multi-Domain Learning: When Do Domains Matter?

By Mahesh Joshi, Mark Dredze, William W. Cohen, Carolyn Penstein Rosé. 2012

74 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

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In Representation & Self-Supervised Learning

Citations in the corpus

Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges (2019, background): “Many-to-one multilingual NMT can be categorized as a multi-domain 3 learning problem (Dredze et al., 2010; Joshi et al., 2012; Nam and Han, 2015), where the task of translating into a selected language remains the same, but the input distribution is different across source languages (Arivazhagan et…”
Learning Multi-domain Convolutional Neural Networks for Visual Tracking (2015, methodology): “This section describes our CNN architecture and multi-domain learning approach to obtain domain-independent representations for visual tracking.”
Deep Multi-task Representation Learning: A Tensor Factorisation Approach (2016, background): “Multi-domain learning [Dredze et al., 2010, Joshi et al., 2012] is a relatively independent line of research to multi-task learning.”
Multi-domain learning and generalization in dialog state tracking (2013, methodology): “Each feature vector is augmented to include an indicator of the domain d ( i ) from which it originated, as this has been found to confer much of the beneﬁt of more complex MDL algorithms (Joshi et al., 2012).”
Towards Universal Object Detection by Domain Attention (2019, background): “Multi-Domain Learning/Adaptation: Multi-domain learning (MDL) addresses the learning of representations for multiple domains, known a priori [20, 36].”
A Unified Perspective on Multi-Domain and Multi-Task Learning (2014, background): “Multi-Domain Learning Multi-Domain Learning (MDL) (Dredze et al., 2010; Joshi et al., 2012) shares properties of both domain adaptation and multi-task learning.”
Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data (2014, unknown): “Our work also touches multi-domain learning (Joshi et al., 2012).”
Computational Sociolinguistics: A Survey (2016, background): “On the other hand, much work in the CL community has focused on structured modeling approaches that take a step towards addressing these issues [134, 135].”
Multi-task Domain Adaptation for Sequence Tagging (2016, background): “One concern is that the performance gains only come from additional training data, instead of the deliberately designed framework (Joshi et al., 2012).”
A Survey of Unsupervised Deep Domain Adaptation (2018, background): “Multi-domain learning [62, 113] and multi-task learning [29] are related to transfer learning and domain adaptation.”
Adversarial Transfer Learning (2018, background): “Multi-domain learning can take advantage of the entire dataset to learn individual user preferences [31, 61].”