Multi-features Enhanced Multi-task Learning for V ietnamese Treebank Conversion (2024)

Abstract: “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.”

## introduction dependency parsing, as a fundamental task of natural language processing, aims at capturing syntactic and semantic information between two words through a dependency tree. the top of figure 1 shows a dependency tree from the universal dependency treebank, where the directed edge from the headword "mừng (happy)" to the modified word "mà (and)" is a dependency arc, the label "mark" on the arc indicates the type of dependency relation between two words. recently, dependency parsing has gained more attention due to its simple and understandable structure. the results of dependency parsing have been applied to various artificial intelligence tasks, such as grammatical error correction @xcite , machine translation @xcite , and semantic role labeling @xcite . 2 related work ## our proposed approach treebank conversion aims to convert the source-side tree into the target-side tree. given the word sequence @xmath0. . . w n of the sentence, the part-of-speech sequence @xmath1. . . t n and the source-side tree d src , the task of treebank conversion is to output a syntactic tree d tgt that conforms to the target-side norms. therefore, effectively utilizing the information from the source-side treebank to guide the treebank conversion is key to the research. naturally, we consider encoding the source-side treebank and using it as an additional input to the mlp to enhance the model's conversion capability. specifically, we employ two strategies, pattern embedding(pe) and tree bilstm, to encode the source-side tree from both local and global perspectives. next, we will introduce the main components of the model, including the input layer, encoder layer, and decoder layer. ## conclusion in this work, we presented a multi-feature enhanced multi-task learning framework for vietnamese treebank conversion, addressing the challenges of limited corpora and the scarcity of large-scale annotated data in vietnamese dependency parsing. our approach leverages tree bilstm and pattern embedding to capture both global and local syntactic features from the source treebank, and integrates these features into a multi-task learning framework, enhancing the representation capability for vietnamese. the experiments conducted on benchmark datasets demonstrate the effectiveness of our model, which not only facilitates the conversion of heterogeneous vietnamese treebanks into a unified one but also significantly improves the vietnamese dependency parsing accuracy.