This work uses sequence-to-sequence (seq2seq) models pre-trained on monolingual corpora for machine translation. We pre-train two seq2seq with the source and target languages, then combine encoder of language model decoder model, i.e., cross-connection. add an intermediate layer between to help mapping each other since modules are completely independently. These can as a multilingual because on...

Traditional machine learning methods are difficult to accurately forecast oil production when development measures change. A method of reservoir prediction based on normalized mutual information and a long short-term memory-based sequence-to-sequence model (Seq2Seq-LSTM) was proposed considering the influence liquid well spacing density. First, marine sandstone reservoirs in Y basin were taken ...

Wireless sensor networks (WSNs) are widely used in various fields, and their deployment is critical to ensure area coverage full network connectivity achieve the maximum lifetime. In this study, we present a mixed-integer programming (MIP) model that deeply investigates parameters optimize lifetime analyze connectivity. We further obtained results using Deep Belief Network (DBN) Neural (DNN) al...

There are about 6,000 languages worldwide, most of which low-resource languages. Although the current speech synthesis (or text-to-speech, TTS) for major (e.g., Mandarin, English, French) has achieved good results, voice quality TTS Tibetan) still needs to be further improved. Because prosody plays a significant role in natural speech, article proposes two sequence-to-sequence (seq2seq) Tibetan...

We propose a novel deep neural network architecture for speech recognition that explicitly employs knowledge of the background environmental noise within a deep neural network acoustic model. A deep neural network is used to predict the acoustic environment in which the system in being used. The discriminative embedding generated at the bottleneck layer of this network is then concatenated with...

While Math Word Problem (MWP) solving has emerged as a popular field of study and made great progress in recent years, most existing methods are benchmarked solely on one or two datasets implemented with different configurations. In this paper, we introduce the first open-source library for MWPs called MWPToolkit, which provides unified, comprehensive, extensible framework research purpose. Spe...

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go. Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theor...