A working model for textual Membership Query Synthesis
Frédéric Piedboeuf (fred (point) piedboeuf <at> gmail (point) com)
Wednesday 9 March 2022 at 11:30 AM
Réunion Zoom, below
* La présentation sera donnée en français.
Membership Query Synthesis (MQS) is an active learning paradigm in which one labels generated artificial examples instead of genuine ones to extend a dataset. Despite prodigious advances in the power of generative models, an essential component of MQS, the field stays severely under-studied, especially in the textual domain. In this paper, we show that the previous SOTA on textual MQS, which was tested on a curated dataset of short sentences, performs poorly when used on a real dataset. We propose and report better results than randomly selecting unlabelled data with random selection in the latent space of a VAE coupled with a simple set of filtering mechanisms. This provides an improvement of 31.1% over the previous MQS SOTA on the SST-2 dataset, and of 2.7% over random active learning. To the best of our knowledge, this is the first time MQS is reported to work on a textual task with no constraints on the size of the sentences.
To receive weekly talk announcements, please send an e-mail to firstname.lastname@example.org. 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