Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
[HTML][HTML] Progress in machine translation
After more than 70 years of evolution, great achievements have been made in machine
translation. Especially in recent years, translation quality has been greatly improved with the …
translation. Especially in recent years, translation quality has been greatly improved with the …
The hitchhiker's guide to testing statistical significance in natural language processing
Statistical significance testing is a standard statistical tool designed to ensure that
experimental results are not coincidental. In this opinion/theoretical paper we discuss the …
experimental results are not coincidental. In this opinion/theoretical paper we discuss the …
Glyce: Glyph-vectors for chinese character representations
It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the
use of the glyph information in those languages. However, due to the lack of rich …
use of the glyph information in those languages. However, due to the lack of rich …
Not just privacy: Improving performance of private deep learning in mobile cloud
The increasing demand for on-device deep learning services calls for a highly efficient
manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity …
manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity …
Semantic neural machine translation using AMR
It is intuitive that semantic representations can be useful for machine translation, mainly
because they can help in enforcing meaning preservation and handling data sparsity (many …
because they can help in enforcing meaning preservation and handling data sparsity (many …
Learning to remember translation history with a continuous cache
Existing neural machine translation (NMT) models generally translate sentences in isolation,
missing the opportunity to take advantage of document-level information. In this work, we …
missing the opportunity to take advantage of document-level information. In this work, we …
Is word segmentation necessary for deep learning of Chinese representations?
Segmenting a chunk of text into words is usually the first step of processing Chinese text, but
its necessity has rarely been explored. In this paper, we ask the fundamental question of …
its necessity has rarely been explored. In this paper, we ask the fundamental question of …
Generating sentences from disentangled syntactic and semantic spaces
Variational auto-encoders (VAEs) are widely used in natural language generation due to the
regularization of the latent space. However, generating sentences from the continuous latent …
regularization of the latent space. However, generating sentences from the continuous latent …
Reinforcement-learning-guided source code summarization using hierarchical attention
Code summarization (aka comment generation) provides a high-level natural language
description of the function performed by code, which can benefit the software maintenance …
description of the function performed by code, which can benefit the software maintenance …