Papers with English-Vietnamese
Overcoming the Rare Word Problem for low-resource language pairs in Neural Machine Translation (D19-52)
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| Challenge: | Despite above approaches can improve the prediction of rare words, they still have challenges which have adverse effects on its effectiveness. |
| Approach: | They propose three ways to address rare-word problem in neural machine translation systems . they propose an algorithm to learn morphology of unknown words for English in supervised way to minimize adverse effect of rare- word problem. |
| Outcome: | The proposed approaches improve accuracy on two low-resource language pairs. |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations (N19-1)
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| Challenge: | Syntax integration has been demonstrated highly effective in neural machine translation (NMT). |
| Approach: | They propose a method to integrate source-side syntax implicitly for neural machine translation . they use hidden representations of a well-trained end-to-end dependency parser to concatenate them with ordinary word embeddings to enhance basic NMT models. |
| Outcome: | The proposed method outperforms existing methods on two translation tasks . it can be easily integrated into the widely-used sequence-to-sequence (Seq2Sequen) framework . |
Enriching Biomedical Knowledge for Low-resource Language Through Large-scale Translation (2023.eacl-main)
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| Challenge: | Biomedical data and benchmarks are highly valuable but limited in low-resource languages such as English. |
| Approach: | They propose a translation model in Vietnamese that trains a pretrained Encoder-Decoder Transformer model on 20 million translated abstracts. |
| Outcome: | The proposed model can translate and produce both pretrained and supervised biomedical data in two biomedically important domains. |
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation models treat decoding at each time step equally with the same matrix . conventional methods treat decoder outputs at all time steps with the identical weight matrix causing inaccuracy . |
| Approach: | They propose a model with a mechanism to control the softness of attention by means of an attention temperature. |
| Outcome: | The proposed model outperforms baseline models on Chinese-English and English-Vietnamese translations. |
Distilling Knowledge Learned in BERT for Text Generation (2020.acl-main)
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| Challenge: | Large-scale pre-trained language models such as BERT have revolutionized the state of the art in many language understanding tasks. |
| Approach: | They propose a conditional masked language modeling approach to fine tune BERT on target generation tasks by imposing global sequence-level supervision on conventional Seq2Seq models. |
| Outcome: | The proposed model outperforms strong Transformer baselines on multiple language generation tasks such as machine translation and text summarization. |