Iterative Recursive Attention Model for Interpretable Sequence Classification (CROSBI ID 674276)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Tutek, Martin ; Šnajder, Jan
engleski
Iterative Recursive Attention Model for Interpretable Sequence Classification
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
Natural language processing ; Deep learning ; interpretability
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
249-257.
2018.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
Linzen, Tal ; Chrupała, Grzegorz ; Alishahi, Afra
Brisel: Association for Computational Linguistics (ACL)
Podaci o skupu
EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
poster
01.11.2018-01.11.2018
Bruxelles, Belgija