@inproceedings{bradbury-socher-2017-towards,
title = "Towards Neural Machine Translation with Latent Tree Attention",
author = "Bradbury, James and
Socher, Richard",
editor = "Chang, Kai-Wei and
Chang, Ming-Wei and
Srikumar, Vivek and
Rush, Alexander M.",
booktitle = "Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4303",
doi = "10.18653/v1/W17-4303",
pages = "12--16",
abstract = "Building models that take advantage of the hierarchical structure of language without a priori annotation is a longstanding goal in natural language processing. We introduce such a model for the task of machine translation, pairing a recurrent neural network grammar encoder with a novel attentional RNNG decoder and applying policy gradient reinforcement learning to induce unsupervised tree structures on both the source and target. When trained on character-level datasets with no explicit segmentation or parse annotation, the model learns a plausible segmentation and shallow parse, obtaining performance close to an attentional baseline.",
}
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%0 Conference Proceedings
%T Towards Neural Machine Translation with Latent Tree Attention
%A Bradbury, James
%A Socher, Richard
%Y Chang, Kai-Wei
%Y Chang, Ming-Wei
%Y Srikumar, Vivek
%Y Rush, Alexander M.
%S Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F bradbury-socher-2017-towards
%X Building models that take advantage of the hierarchical structure of language without a priori annotation is a longstanding goal in natural language processing. We introduce such a model for the task of machine translation, pairing a recurrent neural network grammar encoder with a novel attentional RNNG decoder and applying policy gradient reinforcement learning to induce unsupervised tree structures on both the source and target. When trained on character-level datasets with no explicit segmentation or parse annotation, the model learns a plausible segmentation and shallow parse, obtaining performance close to an attentional baseline.
%R 10.18653/v1/W17-4303
%U https://aclanthology.org/W17-4303
%U https://doi.org/10.18653/v1/W17-4303
%P 12-16
Markdown (Informal)
[Towards Neural Machine Translation with Latent Tree Attention](https://aclanthology.org/W17-4303) (Bradbury & Socher, 2017)
ACL