There is abundance of digitised texts available in Sanskrit. However, the word segmentation task in such texts are challenging due to the issue of Sandhi. In Sandhi, words in a sentence often fuse together to form a single chunk of text, where the word delimiter vanishes and sounds at the word boundaries undergo transformations, which is also reflected in the written text. Here, we propose an a...

Sequences have become first class citizens in supervised learning thanks to the resurgence of recurrent neural networks. Many complex tasks that require mapping from or to a sequence of observations can now be formulated with the sequence-to-sequence (seq2seq) framework which employs the chain rule to efficiently represent the joint probability of sequences. In many cases, however, variable siz...

Relation extraction models based on deep learning have been attracting a lot of attention recently. Little research is carried out to reduce their need of labeled training data. In this work, we propose an unsupervised pre-training method based on the sequence-to-sequence model for deep relation extraction models. The pre-trained models need only half or even less training data to achieve equiv...

Writing style is an important indicator of a writer’s persona. In the age of intelligent chatbots, writing style conversion can enable intimate human-AI interaction, allowing us to bridge the inherent gap between AI agents and human beings. In this paper, we apply sequence to sequence neural machine translation model with global attention mechanism to two writing style conversion tasks, mostly ...

We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the output sequence. A similar phenomenon is observable in human language communication. For example, humans tend to repeat entity names or even long phrases in conversation. The challenge with regard to copying in Seq2S...

We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using Manta Ray Foraging Optimization (MRFO) feature selection select model parameters. Features are employed as potential inputs Long Short-Term Memory a seq2seq autoencoder system in final GSR prediction. Six sol...

With the rapid development of IoT, big data and artificial intelligence, research application data-driven hydrological models are increasing. However, when conducting time series analysis, many prediction often directly based on following assumptions: hydrologic normal, homogeneous, smooth non-trending, which not always all true. To address related issues, a solution for short-term forecasting ...

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have proposed, which can generate sequence description. However, the existing ignore global relationships among API methods, are important for understanding usage APIs. this paper, we propose to model dependencie...