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        <title>Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion</title>
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    <name type="personal">
        <namePart type="given">Zhang</namePart>
        <namePart type="family">Zhenguo</namePart>
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        <namePart type="given">Liu</namePart>
        <namePart type="family">Jianjian</namePart>
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    <name type="personal">
        <namePart type="given">Ying</namePart>
        <namePart type="family">Li</namePart>
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        <dateIssued>2024-07</dateIssued>
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            <title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</title>
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        <name type="personal">
            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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        <name type="personal">
            <namePart type="given">Jiye</namePart>
            <namePart type="family">Liang</namePart>
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        <name type="personal">
            <namePart type="given">Zhiyuan</namePart>
            <namePart type="family">Liu</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <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.”</abstract>
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        <url>https://aclanthology.org/2024.ccl-1.80/</url>
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        <date>2024-07</date>
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            <start>1035</start>
            <end>1046</end>
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%0 Conference Proceedings
%T Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion
%A Zhenguo, Zhang
%A Jianjian, Liu
%A Li, Ying
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F zhenguo-etal-2024-multi
%X “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.”
%U https://aclanthology.org/2024.ccl-1.80/
%P 1035-1046

Markdown (Informal)

[Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion](https://aclanthology.org/2024.ccl-1.80/) (Zhenguo et al., CCL 2024)

• Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion (Zhenguo et al., CCL 2024)

ACL

• Zhang Zhenguo, Liu Jianjian, and Ying Li. 2024. Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 1035–1046, Taiyuan, China. Chinese Information Processing Society of China.