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Multi-task learning

Multi-task learning (MTL) is an approach to machine learning that learns a problem together with other related problems at the same time, using a… 
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Papers overview

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2017
2017
Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims… 
2017
2017
Analyzing people’s opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for… 
2013
2013
We present a general regularization-based framework for Multi-task learning (MTL), in which the similarity between tasks can be… 
2011
2011
This paper addresses the problem of learning multiple spoken language understanding (SLU) tasks that have overlapping sets of… 
2010
2010
Multi-task learning aims at combining information across tasks to boost prediction performance, especially when the number of… 
2008
2008
This paper describes our supervised approach to the opinionated and the polarity subtasks in the NTCIR-7 MOAT Challenge. We apply… 
1990
1990
This paper presents a description and an empirical evaluation of a rule-based, cumulative learning system called CSM (classifier…