Papers by Chunpeng Ma
Improving Neural Machine Translation with Neural Syntactic Distance (N19-1)
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| Challenge: | Neural syntactic distance (NSD) is used to represent constituent trees using a sequence whose length is identical to the number of words in the sentence. |
| Approach: | They propose five strategies to improve NMT with explicit use of syntactic information . et al., 2014) propose a set of five strategies that incorporate syntastic information into the encoder and/or decoder of the baseline model. |
| Outcome: | The proposed strategies improve translation performance of the baseline model (+2.1 (En–Ja), +1.3 (Ja–En), +1.2 (En-Ch), and +1.0 (Ch–En) BLEU. |
On the (In)Effectiveness of Images for Text Classification (2021.eacl-main)
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| Challenge: | Existing studies have focused on text classification, but have shown that images do not improve NLP tasks. |
| Approach: | They focus on text classification, where images complement the text and the Wikipedia page can be in one of a number of different languages. |
| Outcome: | The proposed model trains without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing. |
Forest-Based Neural Machine Translation (P18-1)
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| Challenge: | Compared with string-to-string systems, tree-based NMT methods use more syntactic information and can incorporate prior knowledge. |
| Approach: | They propose a tree-based neural machine translation method that translates a linearized packed forest under a simple sequence-to-sequence framework. |
| Outcome: | The proposed method outperforms tree-based approaches in the BLEU score of the proposed model. |