Towards Neural Machine Translation With Latent Tree Attention

Bradbury James, Socher Richard. Arxiv 2017

[Paper]    
Agentic Applications Attention Mechanism Model Architecture Reinforcement Learning

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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