@inproceedings{hua-etal-2025-bird,
title = "{BIRD}: Bronze Inscription Restoration and Dating",
author = "Hua, Wenjie and
Nguyen, Hoang H and
Ge, Gangyan",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1073/",
pages = "21189--21201",
ISBN = "979-8-89176-332-6",
abstract = "Bronze inscriptions from early China are fragmentary and difficult to date. We introduce BIRD (Bronze Inscription Restoration and Dating), a fully encoded dataset grounded in standard scholarly transcriptions and chronological labels. We further propose an allograph-aware masked language modeling framework that integrates domain- and task-adaptive pretraining with a Glyph Net (GN), which links graphemes and allographs. Experiments show that GN improves restoration, while glyph-biased sampling yields gains in dating."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hua-etal-2025-bird">
<titleInfo>
<title>BIRD: Bronze Inscription Restoration and Dating</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wenjie</namePart>
<namePart type="family">Hua</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hoang</namePart>
<namePart type="given">H</namePart>
<namePart type="family">Nguyen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gangyan</namePart>
<namePart type="family">Ge</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Christos</namePart>
<namePart type="family">Christodoulopoulos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tanmoy</namePart>
<namePart type="family">Chakraborty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carolyn</namePart>
<namePart type="family">Rose</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Violet</namePart>
<namePart type="family">Peng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Suzhou, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-332-6</identifier>
</relatedItem>
<abstract>Bronze inscriptions from early China are fragmentary and difficult to date. We introduce BIRD (Bronze Inscription Restoration and Dating), a fully encoded dataset grounded in standard scholarly transcriptions and chronological labels. We further propose an allograph-aware masked language modeling framework that integrates domain- and task-adaptive pretraining with a Glyph Net (GN), which links graphemes and allographs. Experiments show that GN improves restoration, while glyph-biased sampling yields gains in dating.</abstract>
<identifier type="citekey">hua-etal-2025-bird</identifier>
<location>
<url>https://aclanthology.org/2025.emnlp-main.1073/</url>
</location>
<part>
<date>2025-11</date>
<extent unit="page">
<start>21189</start>
<end>21201</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T BIRD: Bronze Inscription Restoration and Dating
%A Hua, Wenjie
%A Nguyen, Hoang H.
%A Ge, Gangyan
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F hua-etal-2025-bird
%X Bronze inscriptions from early China are fragmentary and difficult to date. We introduce BIRD (Bronze Inscription Restoration and Dating), a fully encoded dataset grounded in standard scholarly transcriptions and chronological labels. We further propose an allograph-aware masked language modeling framework that integrates domain- and task-adaptive pretraining with a Glyph Net (GN), which links graphemes and allographs. Experiments show that GN improves restoration, while glyph-biased sampling yields gains in dating.
%U https://aclanthology.org/2025.emnlp-main.1073/
%P 21189-21201
Markdown (Informal)
[BIRD: Bronze Inscription Restoration and Dating](https://aclanthology.org/2025.emnlp-main.1073/) (Hua et al., EMNLP 2025)
ACL
- Wenjie Hua, Hoang H Nguyen, and Gangyan Ge. 2025. BIRD: Bronze Inscription Restoration and Dating. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 21189–21201, Suzhou, China. Association for Computational Linguistics.