Papers by Yuqian Dai
BERTology for Machine Translation: What BERT Knows about Linguistic Difficulties for Translation (2022.lrec-1)
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| Challenge: | Pre-trained transformer-based models have shown excellent performance in most benchmark tests, but lack a good understanding of the linguistic knowledge of BERT in Neural Machine Translation (NMT). |
| Approach: | They propose to use QE models to analyze BERT's syntactic dependencies and their impact on machine translation quality. |
| Outcome: | The proposed model is able to model with self-attention in the pre-training phase, which improves generalization ability. |