[HTML][HTML] Neural machine translation: A review of methods, resources, and tools
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 …
that aims to translate natural languages using computers. In recent years, end-to-end neural …
Transformer: A general framework from machine translation to others
Abstract Machine translation is an important and challenging task that aims at automatically
translating natural language sentences from one language into another. Recently …
translating natural language sentences from one language into another. Recently …
Dynamic context-guided capsule network for multimodal machine translation
Multimodal machine translation (MMT), which mainly focuses on enhancing text-only
translation with visual features, has attracted considerable attention from both computer …
translation with visual features, has attracted considerable attention from both computer …
Dynamic prediction of traffic incident duration on urban expressways: A deep learning approach based on LSTM and MLP
W Zhu, J Wu, T Fu, J Wang, J Zhang… - Journal of intelligent …, 2021 - ieeexplore.ieee.org
Purpose-Efficient traffic incident management is needed to alleviate the negative impact of
traffic incidents. Accurate and reliable estimation of traffic incident duration is of great …
traffic incidents. Accurate and reliable estimation of traffic incident duration is of great …
A label dependence-aware sequence generation model for multi-level implicit discourse relation recognition
Implicit discourse relation recognition (IDRR) is a challenging but crucial task in discourse
analysis. Most existing methods train multiple models to predict multi-level labels …
analysis. Most existing methods train multiple models to predict multi-level labels …
Neural machine translation: Challenges, progress and future
Abstract Machine translation (MT) is a technique that leverages computers to translate
human languages automatically. Nowadays, neural machine translation (NMT) which …
human languages automatically. Nowadays, neural machine translation (NMT) which …
Type-driven multi-turn corrections for grammatical error correction
Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical
errors. In this aspect, dominant models are trained by one-iteration learning while …
errors. In this aspect, dominant models are trained by one-iteration learning while …
A sequence-to-sequence&set model for text-to-table generation
Recently, the text-to-table generation task has attracted increasing attention due to its wide
applications. In this aspect, the dominant model formalizes this task as a sequence-to …
applications. In this aspect, the dominant model formalizes this task as a sequence-to …
Confidence based bidirectional global context aware training framework for neural machine translation
Most dominant neural machine translation (NMT) models are restricted to make predictions
only according to the local context of preceding words in a left-to-right manner. Although …
only according to the local context of preceding words in a left-to-right manner. Although …
RCRC: A deep neural network for dynamic image reconstruction of electrical impedance tomography
S Ren, R Guan, G Liang, F Dong - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
A deep neural network is proposed for solving the dynamic image reconstruction problems
in electrical impedance tomography (EIT), which can realize the filtering, smoothing, and …
in electrical impedance tomography (EIT), which can realize the filtering, smoothing, and …