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Found 24 papers, 7 papers with code
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT).
Improving Neural Machine Translation by Bidirectional Training
We present a simple and effective pretraining strategy -- bidirectional training (BiT) for neural machine translation.
The USYD-JD Speech Translation System for IWSLT 2021
Our constrained system is based on a pipeline framework, i. e. ASR and NMT.
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
High-dimensional simulation optimization is notoriously challenging.
On the Copying Behaviors of Pre-Training for Neural Machine Translation
In response to this problem, we propose a simple and effective method named copying penalty to control the copying behaviors in decoding.
Progressive Multi-Granularity Training for Non-Autoregressive Translation
Non-autoregressive translation (NAT) significantly accelerates the inference process via predicting the entire target sequence.
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation
Results demonstrate that the proposed approach can significantly and universally improve translation quality by reducing translation errors on low-frequency words.
Self-Guided Curriculum Learning for Neural Machine Translation
In the field of machine learning, the well-trained model is assumed to be able to recover the training labels, i. e. the synthetic labels predicted by the model should be as close to the ground-truth labels as possible.
Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding
Spoken language understanding (SLU) system usually consists of various pipeline components, where each component heavily relies on the results of its upstream ones.
Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation
Dialogue generation models face the challenge of producing generic and repetitive responses.
SLUA: A Super Lightweight Unsupervised Word Alignment Model via Cross-Lingual Contrastive Learning
Recently, the performance of the neural word alignment models has exceeded that of statistical models.
Unsupervised Word Alignment via Cross-Lingual Contrastive Learning
Recently, the performance of the neural word alignment models has exceeded that of statistical models.
Understanding and Improving Lexical Choice in Non-Autoregressive Translation
To this end, we introduce an extra Kullback-Leibler divergence term derived by comparing the lexical choice of NAT model and that embedded in the raw data.
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
Encoder layer fusion (EncoderFusion) is a technique to fuse all the encoder layers (instead of the uppermost layer) for sequence-to-sequence (Seq2Seq) models, which has proven effective on various NLP tasks.
Context-Aware Cross-Attention for Non-Autoregressive Translation
Non-autoregressive translation (NAT) significantly accelerates the inference process by predicting the entire target sequence.
Sample and Computationally Efficient Simulation Metamodeling in High Dimensions
Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of a complex simulation model.
Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual Patterns
In response to this issue, we propose to expose explicit cross-lingual patterns, \textit{e. g.} word alignments and generation score, to our proposed zero-shot models.
SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling
Slot filling and intent detection are two main tasks in spoken language understanding (SLU) system.
Overcoming the Curse of Dimensionality in Density Estimation with Mixed Sobolev GANs
We propose a novel GAN framework for non-parametric density estimation with high-dimensional data.
Self-Attention with Cross-Lingual Position Representation
Position encoding (PE), an essential part of self-attention networks (SANs), is used to preserve the word order information for natural language processing tasks, generating fixed position indices for input sequences.
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
Despite their success, kernel methods suffer from a massive computational cost in practice.
Recurrent Graph Syntax Encoder for Neural Machine Translation
Syntax-incorporated machine translation models have been proven successful in improving the model's reasoning and meaning preservation ability.
The University of Sydney's Machine Translation System for WMT19
This paper describes the University of Sydney's submission of the WMT 2019 shared news translation task.
Ranked #1 on
Machine Translation
on WMT2019 Finnish-English
Efficient Learning of Optimal Markov Network Topology with k-Tree Modeling
The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic system by Markov trees can achieve the minimum information loss with the topology of a maximum spanning tree.