Fine-Grained Domain Adaptation for Chinese Syntactic Processing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AUF6DNFKL" target="_blank" >RIV/00216208:11320/23:UF6DNFKL - isvavai.cz</a>

Result on the web

<a href="https://dl.acm.org/doi/10.1145/3629519" target="_blank" >https://dl.acm.org/doi/10.1145/3629519</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3629519" target="_blank" >10.1145/3629519</a>

Result language

angličtina

Original language name

Fine-Grained Domain Adaptation for Chinese Syntactic Processing

Original language description

"Syntactic processing is fundamental to natural language processing. It provides rich and comprehensive syntax information in sentences that could be potentially beneficial for downstream tasks. Recently, pretrained language models have shown great success in Chinese syntactic processing, which typically involves word segmentation, POS tagging, and dependency parsing. However, the on-going research never ends since performance would be degraded drastically when tested on a highly-discrepant domain. This problem is widely accepted as domain adaptation, where the test domain differs from the training domain in supervised learning. Self-training is one promising solution for it, and straightforward source-to-target adaptation has already shown remarkable effectiveness in previous work. While this strategy ignores the fact that sentences of the target domain sentences may have very different gaps from the source training domain. More specifically, sentences with large gaps might fail by direct self-training adaptation. To this end, we propose fine-grained domain adaptation for Chinese syntactic processing in this work, aiming to model the gaps between the source and the target domains accurately and progressively. The key idea is to divide the target domain into fine-grained subdomains by using a specified domain distance metric, and then perform gradual self-training on the subdomains. We further offer an intuitive theoretical illustration based on the theory of Kumar et al. (2020) approximately. In addition, a novel representation learning framework is proposed to encode fine-grained subdomains effectively, aiming to utilize the above idea fully. Experimental results on benchmark datasets show that our method can achieve significant improvements over a variety of baselines."

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

"ACM Transactions on Asian and Low-Resource Language Information Processing"

ISSN

2375-4699

e-ISSN

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Volume of the periodical

22

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

24

Pages from-to

1-24

UT code for WoS article

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EID of the result in the Scopus database

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