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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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    <abstract>Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost-aware sequence-to-sequence learning. In experiments on interactive neural machine translation, we find that the self-regulator discovers an ε-greedy strategy for the optimal cost-quality trade-off by mixing different feedback types including corrections, error markups, and self-supervision. Furthermore, we demonstrate its robustness under domain shift and identify it as a promising alternative to active learning.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1029</identifier>
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%0 Conference Proceedings
%T Self-Regulated Interactive Sequence-to-Sequence Learning
%A Kreutzer, Julia
%A Riezler, Stefan
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F kreutzer-riezler-2019-self
%X Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost-aware sequence-to-sequence learning. In experiments on interactive neural machine translation, we find that the self-regulator discovers an ε-greedy strategy for the optimal cost-quality trade-off by mixing different feedback types including corrections, error markups, and self-supervision. Furthermore, we demonstrate its robustness under domain shift and identify it as a promising alternative to active learning.
%R 10.18653/v1/P19-1029
%U https://aclanthology.org/P19-1029/
%U https://doi.org/10.18653/v1/P19-1029
%P 303-315

Markdown (Informal)

[Self-Regulated Interactive Sequence-to-Sequence Learning](https://aclanthology.org/P19-1029/) (Kreutzer & Riezler, ACL 2019)

• Self-Regulated Interactive Sequence-to-Sequence Learning (Kreutzer & Riezler, ACL 2019)

ACL

• Julia Kreutzer and Stefan Riezler. 2019. Self-Regulated Interactive Sequence-to-Sequence Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 303–315, Florence, Italy. Association for Computational Linguistics.