Neural machine translation: A review
F Stahlberg - Journal of Artificial Intelligence Research, 2020 - jair.org
The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …
natural language into another, has experienced a major paradigm shift in recent years …
Neural machine translation and sequence-to-sequence models: A tutorial
G Neubig - arXiv preprint arXiv:1703.01619, 2017 - arxiv.org
This tutorial introduces a new and powerful set of techniques variously called" neural
machine translation" or" neural sequence-to-sequence models". These techniques have …
machine translation" or" neural sequence-to-sequence models". These techniques have …
Mem2seq: Effectively incorporating knowledge bases into end-to-end task-oriented dialog systems
End-to-end task-oriented dialog systems usually suffer from the challenge of incorporating
knowledge bases. In this paper, we propose a novel yet simple end-to-end differentiable …
knowledge bases. In this paper, we propose a novel yet simple end-to-end differentiable …
Document-level neural machine translation with hierarchical attention networks
Neural Machine Translation (NMT) can be improved by including document-level contextual
information. For this purpose, we propose a hierarchical attention model to capture the …
information. For this purpose, we propose a hierarchical attention model to capture the …
Global-to-local memory pointer networks for task-oriented dialogue
End-to-end task-oriented dialogue is challenging since knowledge bases are usually large,
dynamic and hard to incorporate into a learning framework. We propose the global-to-local …
dynamic and hard to incorporate into a learning framework. We propose the global-to-local …
Adaptive memory networks with self-supervised learning for unsupervised anomaly detection
Unsupervised anomaly detection aims to build models to effectively detect unseen
anomalies by only training on the normal data. Although previous reconstruction-based …
anomalies by only training on the normal data. Although previous reconstruction-based …
Neural machine translation with deep attention
Deepening neural models has been proven very successful in improving the model's
capacity when solving complex learning tasks, such as the machine translation task …
capacity when solving complex learning tasks, such as the machine translation task …
Asynchronous bidirectional decoding for neural machine translation
The dominant neural machine translation (NMT) models apply unified attentional encoder-
decoder neural networks for translation. Traditionally, the NMT decoders adopt recurrent …
decoder neural networks for translation. Traditionally, the NMT decoders adopt recurrent …
Cm-net: A novel collaborative memory network for spoken language understanding
Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot
filling, which are generally modeled jointly in existing works. However, most existing models …
filling, which are generally modeled jointly in existing works. However, most existing models …