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Found 34 papers, 4 papers with code
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation
In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data.
TranSmart: A Practical Interactive Machine Translation System
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed.
Benchmarking Graph Neural Networks on Link Prediction
In this paper, we benchmark several existing graph neural network (GNN) models on different datasets for link predictions.
Adaptive Spatial-Temporal Inception Graph Convolutional Networks for Multi-step Spatial-Temporal Network Data Forecasting
Spatial-temporal data forecasting is of great importance for industries such as telecom network operation and transportation management.
Hierarchical Representation via Message Propagation for Robust Model Fitting
In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the consensus analysis and the preference analysis to estimate the parameters of multiple model instances from data corrupted by outliers, for robust model fitting.
Non-Newtonian and poroelastic effects in simulations of arterial flows
In this paper, we focus on investigating the influence on hydrodynamic factors of different coupled computational models describing the interaction between an incompressible fluid and two symmetric elastic or poroelastic structures.
Fluid Dynamics Numerical Analysis Numerical Analysis
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation
In addition, experimental results demonstrate that our Multi-Task NAT is complementary to knowledge distillation, the standard knowledge transfer method for NAT.
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data.
Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation
First, we train an identification model on the original training data, and use it to distinguish inactive examples and active examples by their sentence-level output probabilities.
Reannealing of Decaying Exploration Based On Heuristic Measure in Deep Q-Network
Existing exploration strategies in reinforcement learning (RL) often either ignore the history or feedback of search, or are complicated to implement.
Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market.
Cross Learning in Deep Q-Networks
In this work, we propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods, particularly in the deep Q-networks where the overestimation is exaggerated by function approximation errors.
How Does Selective Mechanism Improve Self-Attention Networks?
Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words.
Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models
In this paper, we bridge the gap by assessing the bilingual knowledge learned by NMT models with phrase table -- an interpretable table of bilingual lexicons.
Neuron Interaction Based Representation Composition for Neural Machine Translation
Starting from this intuition, we propose a novel approach to compose representations learned by different components in neural machine translation (e. g., multi-layer networks or multi-head attention), based on modeling strong interactions among neurons in the representation vectors.
Multi-Granularity Self-Attention for Neural Machine Translation
Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information.
Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons
Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work.
Self-Attention with Structural Position Representations
Although self-attention networks (SANs) have advanced the state-of-the-art on various NLP tasks, one criticism of SANs is their ability of encoding positions of input words (Shaw et al., 2018).
One Model to Learn Both: Zero Pronoun Prediction and Translation
In this paper, we propose a unified and discourse-aware ZP translation approach for neural MT models.
Towards Understanding Neural Machine Translation with Word Importance
Although neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, the interpretability of NMT remains unsatisfactory.
Exploiting Sentential Context for Neural Machine Translation
In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT).
Multiple Independent Subspace Clusterings
To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering.
Modeling Recurrence for Transformer
In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention and recurrent networks.
Information Aggregation for Multi-Head Attention with Routing-by-Agreement
Multi-head attention is appealing for its ability to jointly extract different types of information from multiple representation subspaces.
Context-Aware Self-Attention Networks
Self-attention model have shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies.
Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement
With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation.
Learning to Refine Source Representations for Neural Machine Translation
Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process.
Exploiting Deep Representations for Neural Machine Translation
Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures.
Network Modeling and Pathway Inference from Incomplete Data ("PathInf")
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems.
Translating Phrases in Neural Machine Translation
Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does.
Neural Machine Translation Advised by Statistical Machine Translation
Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years.
Scalable Compression of Deep Neural Networks
Deep neural networks generally involve some layers with mil- lions of parameters, making them difficult to be deployed and updated on devices with limited resources such as mobile phones and other smart embedded systems.
