We study and quantify the generalization patterns of multitask learning (MTL) models for sequence labeling tasks.
Reading the bibliography…
A tutorial on hidden markov models and selected applications in speech recognition
Lawrence R. Rabiner · 1989
Earlier work this paper cites.
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza · 1992
Earlier work this paper cites.
Multitask learning
Rich Caruana · 1997
Earlier work this paper cites.
A model of inductive bias learning
Jonathan Baxter · 2000
Earlier work this paper cites.
Maximum entropy markov models for information extraction and segmentation
Andrew Mccallum, Dayne Freitag, and Fernando Pereira · 2000
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira · 2001
Earlier work this paper cites.
Introduction to the conll-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder · 2003
Earlier work this paper cites.
Regularized multi–task learning
Theodoros Evgeniou and Massimiliano Pontil · 2004
Earlier work this paper cites.
Learning multiple tasks with kernel methods
Theodoros Evgeniou, Charles A. Micchelli, and Massimiliano Pontil · 2005
Earlier work this paper cites.
Bounds for linear multi-task learning
Andreas Maurer · 2006
Earlier work this paper cites.
Multi-task gaussian process prediction
Edwin V. Bonilla, Kian Ming A. Chai, and Christopher K. I. Williams · 2007
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
Earlier work this paper cites.
Large margin multi-task metric learning
Shibin Parameswaran and Kilian Weinberger · 2010
Earlier work this paper cites.
Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance
Nguyen Xuan Vinh, Julien Epps, and James Bailey · 2010
Cited alongside, same era.
Learning with whom to share in multi-task feature learning
Zhuoliang Kang, Kristen Grauman, and Fei Sha · 2011
Cited alongside, same era.
Hierarchical multitask structured output learning for large-scale sequence segmentation
Nico Görnitz, Christian Widmer, Georg Zeller, Andre Kahles, Sören Sonnenburg, and Gunnar Rätsch · 2011
Cited alongside, same era.
Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa · 2011
Cited alongside, same era.
A regularization approach to learning task relationships in multitask learning
Yu Zhang and Dit-Yan Yeung · 2014
Cited alongside, same era.
The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
Cited alongside, same era.
Algorithm-dependent generalization bounds for multi-task learning
Tongliang Liu, Dacheng Tao, Mingli Song, and Stephen J. Maybank · 2016
Cited alongside, same era.
A neural network multi-task learning approach to biomedical named entity recognition
Gamal Crichton, Sampo Pyysalo, Billy Chiu, and Anna Korhonen · 2017
Cited alongside, same era.
Consistent multitask learning with nonlinear output relations
Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, and Massimiliano Pontil · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
Cited alongside, same era.
Multi-task learning for sequence tagging: An empirical study
Soravit Changpinyo, Hexiang Hu, and Fei Sha · 2018
Cited alongside, same era.
Deep asymmetric multi-task feature learning
Haebeom Lee, Eunho Yang, and Sung Hwang · 2018
Later among the works it cites.
Where are we now? a large benchmark study of recent symbolic regression methods
Patryk Orzechowski, William La Cava, and Jason H. Moore · 2018
Later among the works it cites.
A hierarchical multi-task approach for learning embeddings from semantic tasks
Victor Sanh, Thomas Wolf, and Sebastian Ruder · 2019
Later among the works it cites.
Latent multi-task architecture learning
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard · 2019
Later among the works it cites.
Bam! born-again multi-task networks for natural language understanding
Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, and Quoc V. Le · 2019
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
Gp learn, 2019
Trevor Stephens · 2019
Later among the works it cites.
Learning word vectors for 157 languages, 2020
Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov · 2020
Closest in time.
Conlleval-python, 2020
Sighsmile · 2020
Closest in time.
Identifying beneficial task relations for multi-task learning in deep neural networks
Joachim Bingel and Anders Søgaard · 2026
Closest in time.
Deep multi-task learning with low level tasks supervised at lower layers
Anders Søgaard and Yoav Goldberg · 2038
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…