“Pre-trained language representation-based dependency parsing models have achieved obviousimprovements in rich-resource languages. However, these model performances depend on thequality and scale of training data significantly. Compared with Chinese and English, the scale ofVietnamese Dependency treebank is scarcity. Considering human annotation is labor-intensiveand time-consuming, we propose a multi-features enhanced multi-task learning framework toconvert all heterogeneous Vietnamese Treebanks to a unified one. On the one hand, we exploitTree BiLSTM and pattern embedding to extract global and local dependency tree features fromthe source Treebank. On the other hand, we propose to integrate these features into a multi-tasklearning framework to use the source dependency parsing to assist the conversion processing.Experiments on the benchmark datasets show that our proposed model can effectively convertheterogeneous treebanks, thus further improving the Vietnamese dependency parsing accuracy byabout 7.12 points in LAS.”

With the strong representational capabilities of pre-trained language models, dependency parsing in resource-rich languages has seen significant advancements. However, the parsing accuracy drops sharply when the model is transferred to low-resource language due to distribution shifts. To alleviate this issue, we propose a representation alignment and adversarial model to filter out useful knowledge from rich-resource language and ignore useless ones. Our proposed model consists of two components, i.e., an alignment network in the input layer for selecting useful language-specific features and an adversarial network in the encoder layer for augmenting the language-invariant contextualized features. Experiments on the benchmark datasets show that our proposed model outperforms RoBERTa-enhanced strong baseline models by 1.37 LAS and 1.34 UAS. Detailed analysis shows that both alignment and adversarial networks are equally important in alleviating the distribution shifts problem and can complement each other. In addition, the comparative experiments demonstrate that both the alignment and adversarial networks can substantially facilitate extracting and utilizing relevant target language features, thereby increasing the adaptation capability of our proposed model.