Papers by Yimeng Zhuang
Token-level Dynamic Self-Attention Network for Multi-Passage Reading Comprehension (P19-1)
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| Challenge: | Multi-passage reading comprehension requires the ability to combine cross-passages information and reason over multiple passages to infer the answer. |
| Approach: | They propose a Dynamic Self-attention Network (DynSAN) which processes cross-passage information at token-level and meanwhile avoids substantial computational costs. |
| Outcome: | The proposed model achieves state-of-the-art performance on the SearchQA, Quasar-T and WikiHop datasets and further ablation validates the effectiveness of its components. |
Pretrained Bidirectional Distillation for Machine Translation (2023.acl-long)
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| Challenge: | Existing studies have focused on language knowledge transfer from pretrained models to neural machine translation models. |
| Approach: | They propose to use masked language pretraining to efficiently transfer bidirectional language knowledge to NMT models. |
| Outcome: | The proposed method can significantly improve machine translation performance and achieve competitive or even better results than previous methods. |
Quantifying Context Overlap for Training Word Embeddings (D18-1)
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| Challenge: | Experimental results show that word embeddings can be improved using word embeds . word embedings are a popular form of natural language processing . |
| Approach: | They propose to estimate second order co-occurrence relations based on context overlap . they use the augmented data to enhance word embeddings learning . |
| Outcome: | The proposed model improves word vectors for word similarity and downstream NLP tasks. |
Long-range Sequence Modeling with Predictable Sparse Attention (2022.acl-long)
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| Challenge: | Existing approaches to capture global context dependencies in sequence modeling suffer from quadratic complexity in time and memory usage. |
| Approach: | They propose an efficient Transformer architecture for fast long-range sequence modeling with a sparse attention matrix and a hidden state cross module. |
| Outcome: | The proposed architecture outperforms the standard multi-head attention and its variants in various long-sequence tasks with low computational costs. |