Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

Many neural network pruning algorithms proceed in three steps: train the network to
completion, remove unwanted structure to compress the network, and retrain the remaining …

Abstract Sign Language Recognition (SLR) has been an active research field for the last two
decades. However, most research to date has considered SLR as a naive gesture …

Current end-to-end machine reading and question answering (Q\&A) models are primarily
based on recurrent neural networks (RNNs) with attention. Despite their success, these …

Deep learning (DL) creates impactful advances following a virtuous recipe: model
architecture search, creating large training data sets, and scaling computation. It is widely …

In this paper, we propose a deep learning based vehicle trajectory prediction technique
which can generate the future trajectory sequence of surrounding vehicles in real time. We …

Unsupervised representation learning algorithms such as word2vec and ELMo improve the
accuracy of many supervised NLP models, mainly because they can take advantage of large …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

We propose a novel methodology to generate domain-specific large-scale question
answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We …