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Learning to Multi-Task Learn for Better Neural Machine Translation
We effectively and efficiently learn the training schedule policy within the imitation learning framework using an oracle policy algorithm that dynamically sets the importance weights of auxiliary tasks based on their contributions to the generalisability of the main NMT task.
Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation
The role of training schedule becomes even more crucial in \textit{biased-MTL} where the goal is to improve one (or a subset) of tasks the most, e. g. translation quality.
Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model
Incorporating syntactic information in Neural Machine Translation (NMT) can lead to better reorderings, particularly useful when the language pairs are syntactically highly divergent or when the training bitext is not large.
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation
The routing network enables adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state.
Neural Machine Translation for Bilingually Scarce Scenarios: A Deep Multi-task Learning Approach
Neural machine translation requires large amounts of parallel training text to learn a reasonable-quality translation model.
Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model
In this paper, we propose a forest-to-sequence Attentional Neural Machine Translation model to make use of exponentially many parse trees of the source sentence to compensate for the parser errors.
