@inproceedings{matsuda-asahara-2026-extending,
title = "Extending omnes flores for the {E}va{L}atin 2026 Dependency Parsing Tasks",
author = "Matsuda, Hiroshi and
Asahara, Masayuki",
editor = "Sprugnoli, Rachele and
Passarotti, Marco",
booktitle = "Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages ({LT}4{HALA} 2026) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.lt4hala-1.47/",
doi = "10.63317/5nmhkkp3qheh",
pages = "448--452",
abstract = "omnes flores is an NLP framework based on Universal Dependencies (UD) that utilizes multilingual Large Language Models (LLMs), and its default model is trained on data from 40 UD languages comprising 40 treebanks. For the EvaLatin 2026 Dependency Parsing Tasks, we extended the training data of omnes flores by incorporating six public Latin treebanks from UD and trained a dependency parsing model using the extended training data. The dependency parser of omnes flores normally takes a list of word FORM values as input. However, since the EvaLatin 2026 test data includes an UPOS column, we investigated whether incorporating both FORM and UPOS during both training and inference could improve parsing accuracy. Our experiments show that training using both FORM and UPOS improves performance by 0.5-1.0 LAS points on Prose compared with training using only FORM, but decreases performance by 5 points on Poetry."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="matsuda-asahara-2026-extending">
<titleInfo>
<title>Extending omnes flores for the EvaLatin 2026 Dependency Parsing Tasks</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hiroshi</namePart>
<namePart type="family">Matsuda</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Masayuki</namePart>
<namePart type="family">Asahara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rachele</namePart>
<namePart type="family">Sprugnoli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marco</namePart>
<namePart type="family">Passarotti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA Language Resources Association (ELRA)</publisher>
<place>
<placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>omnes flores is an NLP framework based on Universal Dependencies (UD) that utilizes multilingual Large Language Models (LLMs), and its default model is trained on data from 40 UD languages comprising 40 treebanks. For the EvaLatin 2026 Dependency Parsing Tasks, we extended the training data of omnes flores by incorporating six public Latin treebanks from UD and trained a dependency parsing model using the extended training data. The dependency parser of omnes flores normally takes a list of word FORM values as input. However, since the EvaLatin 2026 test data includes an UPOS column, we investigated whether incorporating both FORM and UPOS during both training and inference could improve parsing accuracy. Our experiments show that training using both FORM and UPOS improves performance by 0.5-1.0 LAS points on Prose compared with training using only FORM, but decreases performance by 5 points on Poetry.</abstract>
<identifier type="citekey">matsuda-asahara-2026-extending</identifier>
<identifier type="doi">10.63317/5nmhkkp3qheh</identifier>
<location>
<url>https://aclanthology.org/2026.lt4hala-1.47/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>448</start>
<end>452</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Extending omnes flores for the EvaLatin 2026 Dependency Parsing Tasks
%A Matsuda, Hiroshi
%A Asahara, Masayuki
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F matsuda-asahara-2026-extending
%X omnes flores is an NLP framework based on Universal Dependencies (UD) that utilizes multilingual Large Language Models (LLMs), and its default model is trained on data from 40 UD languages comprising 40 treebanks. For the EvaLatin 2026 Dependency Parsing Tasks, we extended the training data of omnes flores by incorporating six public Latin treebanks from UD and trained a dependency parsing model using the extended training data. The dependency parser of omnes flores normally takes a list of word FORM values as input. However, since the EvaLatin 2026 test data includes an UPOS column, we investigated whether incorporating both FORM and UPOS during both training and inference could improve parsing accuracy. Our experiments show that training using both FORM and UPOS improves performance by 0.5-1.0 LAS points on Prose compared with training using only FORM, but decreases performance by 5 points on Poetry.
%R 10.63317/5nmhkkp3qheh
%U https://aclanthology.org/2026.lt4hala-1.47/
%U https://doi.org/10.63317/5nmhkkp3qheh
%P 448-452
Markdown (Informal)
[Extending omnes flores for the EvaLatin 2026 Dependency Parsing Tasks](https://aclanthology.org/2026.lt4hala-1.47/) (Matsuda & Asahara, LT4HALA 2026)
ACL