Papers by Weiyue Wang
uniblock: Scoring and Filtering Corpus with Unicode Block Information (D19-1)
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| Challenge: | Existing methods to remove sentences consisting of illegal characters are tedious and repetitive. |
| Approach: | They propose a statistical method to identify illegal characters in natural language processing . they use a fixed-size feature vector to generate a Gaussian mixture model for each sentence . |
| Outcome: | The proposed method can score sentences and filter corpus on clean corpus and improve performance. |
Neural Language Modeling for Named Entity Recognition (2020.coling-main)
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| Challenge: | Experimental results show that named entity recognition systems are faster and more flexible for the size of the corpus. |
| Approach: | They propose to use a neural language model as an alternative to the conditional random field layer for named entity recognition. |
| Outcome: | The proposed system has a significant speed advantage with a marginal performance degradation. |
Towards a Better Understanding of Label Smoothing in Neural Machine Translation (2020.aacl-main)
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| Challenge: | In recent years, Neural Network (NN) models bring steady and concrete improvements on the task of Machine Translation (MT). |
| Approach: | They propose to penalize over-confident outputs and regularize the model so that its outputs do not diverge too much from some prior distribution. |
| Outcome: | The proposed method is well-motivated and can improve the performance of strong neural machine translation systems. |
Predicting and Using Target Length in Neural Machine Translation (2020.aacl-main)
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| Challenge: | Current NMT systems do not model the length of the output explicitly . length normalization is a common technique used in the beam search of NMT to enable a fair comparison of partial hypotheses with different lengths. |
| Approach: | They propose to use length prediction as an auxiliary task to obtain length information from the encoder. |
| Outcome: | The proposed sub-network improves over the baseline system and the predicted length can be used as an alternative to length normalization during decoding. |
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition (2021.acl-srw)
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| Challenge: | Existing methods for named entity recognition use only a limited number of samples . data augmentation and selftraining are popular methods to generate additional synthetic data . |
| Approach: | They investigate the impact of data augmentation and data augmented on named entity recognition tasks. |
| Outcome: | The proposed methods improve the performance of three named entity recognition tasks. |
Transformer-Based Direct Hidden Markov Model for Machine Translation (2021.acl-srw)
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| Challenge: | Recent studies have found that word alignments produced by the multi-head cross-attention weights are poor. |
| Approach: | They propose to introduce the hidden Markov model to the transformer architecture and introduce alignment components while keeping the system monolithic. |
| Outcome: | The proposed model outperforms the baseline model but is slower in training and decoding. |
Neural Hidden Markov Model for Machine Translation (P18-2)
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| Challenge: | Attention-based neural machine translation models selectively focus on specific source positions to produce a translation. |
| Approach: | They propose to replace the attention component with a neural hidden Markov model that selectively focuss on specific source positions to produce a translation. |
| Outcome: | The proposed model performs better than the state-of-the-art attention-based models on the GermanEnglish and ChineseEnglish translation tasks. |