RALI-OLST Weekly Talks
SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling
Peng Lu (LPXD1101 <at> outlook (point) com)
Wednesday 22 May 2019 at 11:30 AM
Salle 3195, Pavillon André-Aisenstadt
Multi-task learning (MTL) has been studied recently for sequence labeling. Typically, auxiliary tasks are selected specifically in order to improve the performance of a target task. Jointly learning multiple tasks in a way that benefits all of them simultaneously can increase the utility of MTL. In order to do so, we propose a new LSTM cell which contains both shared parameters that can learn from all tasks, and task-specific parameters that can learn task specific information. We name it a Shared-Cell Long-Short Term Memory. Experimental results on three sequence labeling benchmarks (named-entity recognition, text chunking, and part-of-speech tagging) demonstrate the effectiveness of this new type of cell.
To receive weekly talk announcements, please send an e-mail to email@example.com. Simply write a message containing the single line 'subscribe ralli' (without the quotes, with a double 'l' in 'ralli').
See all the weekly talks for the year:1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022