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        <name type="personal">
            <namePart type="given">Tal</namePart>
            <namePart type="family">Linzen</namePart>
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            <namePart type="given">Grzegorz</namePart>
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            <namePart type="given">Afra</namePart>
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    <abstract>Natural language processing has greatly benefited from the introduction of the attention mechanism. However, standard attention models are of limited interpretability for tasks that involve a series of inference steps. We describe an iterative recursive attention model, which constructs incremental representations of input data through reusing results of previously computed queries. We train our model on sentiment classification datasets and demonstrate its capacity to identify and combine different aspects of the input in an easily interpretable manner, while obtaining performance close to the state of the art.</abstract>
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    <identifier type="doi">10.18653/v1/W18-5427</identifier>
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%0 Conference Proceedings
%T Iterative Recursive Attention Model for Interpretable Sequence Classification
%A Tutek, Martin
%A Šnajder, Jan
%Y Linzen, Tal
%Y Chrupała, Grzegorz
%Y Alishahi, Afra
%S Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F tutek-snajder-2018-iterative
%X Natural language processing has greatly benefited from the introduction of the attention mechanism. However, standard attention models are of limited interpretability for tasks that involve a series of inference steps. We describe an iterative recursive attention model, which constructs incremental representations of input data through reusing results of previously computed queries. We train our model on sentiment classification datasets and demonstrate its capacity to identify and combine different aspects of the input in an easily interpretable manner, while obtaining performance close to the state of the art.
%R 10.18653/v1/W18-5427
%U https://aclanthology.org/W18-5427
%U https://doi.org/10.18653/v1/W18-5427
%P 249-257

Markdown (Informal)

[Iterative Recursive Attention Model for Interpretable Sequence Classification](https://aclanthology.org/W18-5427) (Tutek & Šnajder, EMNLP 2018)

• Iterative Recursive Attention Model for Interpretable Sequence Classification (Tutek & Šnajder, EMNLP 2018)

ACL

• Martin Tutek and Jan Šnajder. 2018. Iterative Recursive Attention Model for Interpretable Sequence Classification. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 249–257, Brussels, Belgium. Association for Computational Linguistics.