University of Macau, Natural Language Processing & Portuguese-Chinese Machine Translation (NLP2CT) Laboratory, University of Macau, Macau, China
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Learning Deep Transformer Models for Machine Translation
It is claimed that a truly deep Transformer model can surpass the Transformer-Big counterpart by 1) proper use of layer normalization and 2) a novel way of passing the combination of previous layers to the next.
Norm-Based Curriculum Learning for Neural Machine Translation
This paper aims to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method that uses the norm (aka length or module) of a word embedding as a measure of the difficulty of the sentence, the competence of the model, and the weight of the sentences.
Modeling Localness for Self-Attention Networks
- Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia S. Chao, T. Zhang
- Computer ScienceEMNLP
- 24 October 2018
This work cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention in self-attention networks, to maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies.
UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation
- Liang Tian, Derek F. Wong, Lidia S. Chao, P. Quaresma, Francisco Oliveira, Lu Yi
- Computer ScienceLREC
- 1 May 2014
The acquisition of a large scale and high quality parallel corpora for English and Chinese for Statistical Machine Translation (SMT) is described, designed to embrace eight different domains.
An algorithm for zero-skew clock tree routing with buffer insertion
A clustering-based algorithm which uses shortest delay as the cost function and the problem of finding legal positions for buffers such that no buffers overlap can be formulated as a shortest path problem on graphs, and can be solved by the Bellman-Ford algorithm.
LEPOR: A Robust Evaluation Metric for Machine Translation with Augmented Factors
Experiments show that this novel metric yields the state-of-the-art correlation with human judgments compared with classic metrics BLEU, TER, Meteor-1.3 and two latest metrics (AMBER and MP4IBM1), which proves it a robust one by employing a feature-rich and model-independent approach.
Context-Aware Self-Attention Networks
- Baosong Yang, Jian Li, Derek F. Wong, Lidia S. Chao, Xing Wang, Zhaopeng Tu
- Computer ScienceAAAI
- 15 February 2019
This work proposes to contextualize the transformations of the query and key layers, which are used to calculates the relevance between elements, and leverage the internal representations that embed both global and deep contexts, thus avoid relying on external resources.
Language-independent Model for Machine Translation Evaluation with Reinforced Factors
A novel language-independent evaluation metric is proposed in this work with enhanced factors and optional linguistic information (part-of-speech, n-grammar) but not very much to make the metric perform well on different language pairs.
Graph-based Semi-Supervised Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Empirical results reveal that the proposed graph-based semisupervised joint model of Chinese word segmentation and part-of-speech tagging can yield better results than the supervised baselines and other competitive semi-supervised CRFs in this task.
A Description of Tunable Machine Translation Evaluation Systems in WMT13 Metrics Task
This paper is to describe the machine translation evaluation systems used for participation in the WMT13 shared Metrics Task and two automatic MT evaluation systems nLEPOR_baseline and LEPOR_v3.1.