Continual Learning for Sentence Representations Using Conceptors
This addresses the challenge of catastrophic forgetting in NLP for researchers and practitioners, but it is incremental as it builds on existing continual learning techniques.
The paper tackles the problem of continual learning for sentence representations, aiming to update sentence encoders with new corpora while preserving accuracy on old ones, and shows that the proposed method retains competence on previously encountered corpora in semantic textual similarity tasks.
Distributed representations of sentences have become ubiquitous in natural language processing tasks. In this paper, we consider a continual learning scenario for sentence representations: Given a sequence of corpora, we aim to optimize the sentence encoder with respect to the new corpus while maintaining its accuracy on the old corpora. To address this problem, we propose to initialize sentence encoders with the help of corpus-independent features, and then sequentially update sentence encoders using Boolean operations of conceptor matrices to learn corpus-dependent features. We evaluate our approach on semantic textual similarity tasks and show that our proposed sentence encoder can continually learn features from new corpora while retaining its competence on previously encountered corpora.