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Found 11 papers, 7 papers with code
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation
Building neural machine translation systems to perform well on a specific target domain is a well-studied problem.
Sockeye 3: Fast Neural Machine Translation with PyTorch
When running comparable models, Sockeye 3 is up to 126% faster than other PyTorch implementations on GPUs and up to 292% faster on CPUs.
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation
Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference.
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations
Finally, we use an auxiliary parser (AP) to filter the generated utterances.
From English To Foreign Languages: Transferring Pre-trained Language Models
With a single GPU, our approach can obtain a foreign BERT base model within a day and a foreign BERT large within two days.
Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations
We investigate whether off-the-shelf deep bidirectional sentence representations trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsupervised universal dependency parser.
Inducing Grammars with and for Neural Machine Translation
To address both of these issues we introduce a model that simultaneously translates while inducing dependency trees.
The Importance of Being Recurrent for Modeling Hierarchical Structure
Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks such as language modeling (Linzen et al., 2016) and neural machine translation (Shi et al., 2016).
Examining Cooperation in Visual Dialog Models
In this work we propose a blackbox intervention method for visual dialog models, with the aim of assessing the contribution of individual linguistic or visual components.
Unsupervised Neural Hidden Markov Models
In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model.
Recurrent Memory Networks for Language Modeling
In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data.
