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        <title>Continual Learning for Sentence Representations Using Conceptors</title>
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    <name type="personal">
        <namePart type="given">Tianlin</namePart>
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        <namePart type="family">Ungar</namePart>
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    <name type="personal">
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        <dateIssued>2019-jun</dateIssued>
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            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)</title>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/N19-1331</identifier>
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        <date>2019-jun</date>
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%0 Conference Proceedings
%T Continual Learning for Sentence Representations Using Conceptors
%A Liu, Tianlin
%A Ungar, Lyle
%A Sedoc, João
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F liu-etal-2019-continual
%X 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.
%R 10.18653/v1/N19-1331
%U https://aclanthology.org/N19-1331
%U https://doi.org/10.18653/v1/N19-1331
%P 3274-3279

Markdown (Informal)

[Continual Learning for Sentence Representations Using Conceptors](https://aclanthology.org/N19-1331) (Liu et al., NAACL 2019)

• Continual Learning for Sentence Representations Using Conceptors (Liu et al., NAACL 2019)

ACL

• Tianlin Liu, Lyle Ungar, and João Sedoc. 2019. Continual Learning for Sentence Representations Using Conceptors. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3274–3279, Minneapolis, Minnesota. Association for Computational Linguistics.