@inproceedings{rosa-etal-2026-recognizing,
title = "Recognizing {U}ruguayan {S}ign {L}anguage Glosses in Continuous Discourse: A Curated Dataset and First Baselines",
author = "Ros{\'a}, Manu Bervejillo and
Techera, Facundo and
Lisboa, Emilio Valle and
Stassi, Ariel E. and
Chiruzzo, Luis",
editor = "Singh, Sanjeet and
Joshi, Abhinav and
Dattatreya, Pokala and
Artiaga, Keren and
Pujara, Khushi Jaydeep and
{\v{Z}}elezn{\'y}, Tom{\'a}{\v{s}} and
Gruber, Ivan and
Honz{\'i}k, Jakub and
Kamila, Sabyasachi and
Quiroga, Facundo Manuel and
Hr{\'u}z, Marek and
Hasanuzzaman, Mohammed and
Modi, Ashutosh",
booktitle = "Proceedings of the Second Workshop on Sign Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wslp-1.11/",
pages = "111--120",
ISBN = "979-8-89176-488-0",
abstract = "Uruguayan Sign Language (LSU) is a low-resource language with a lack of computational models. Building on the first gloss-annotated corpus of LSU interpreter videos, we release LSU-446, a curated dataset of 17,793 sign instances over 446 glosses. Unlike standard benchmarks recorded under controlled conditions, LSU-446 consists of interpreted discourse, with an average sign duration of 403{~}ms, and heavy coarticulation. To the best of our knowledge, this work establishes the first isolated sign recognition baselines for LSU at scale, comparing a Transformer (SPOTER) and a graph convolutional network (MSG3D); MSG3D reaches 32.7{\%} Top-1 over 446 classes and, unlike SPOTER, degrades only marginally as the vocabulary grows. We then use both architectures for a sign spotting experiment on a held-out video from an unseen signer and domain (33.8{\%} out-of-vocabulary), comparing a segmenter-based pipeline against a sliding window, under an evaluation protocol that decouples temporal detection from lexical recognition."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rosa-etal-2026-recognizing">
<titleInfo>
<title>Recognizing Uruguayan Sign Language Glosses in Continuous Discourse: A Curated Dataset and First Baselines</title>
</titleInfo>
<name type="personal">
<namePart type="given">Manu</namePart>
<namePart type="given">Bervejillo</namePart>
<namePart type="family">Rosá</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Facundo</namePart>
<namePart type="family">Techera</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emilio</namePart>
<namePart type="given">Valle</namePart>
<namePart type="family">Lisboa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ariel</namePart>
<namePart type="given">E</namePart>
<namePart type="family">Stassi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Luis</namePart>
<namePart type="family">Chiruzzo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Second Workshop on Sign Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sanjeet</namePart>
<namePart type="family">Singh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abhinav</namePart>
<namePart type="family">Joshi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pokala</namePart>
<namePart type="family">Dattatreya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Keren</namePart>
<namePart type="family">Artiaga</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khushi</namePart>
<namePart type="given">Jaydeep</namePart>
<namePart type="family">Pujara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tomáš</namePart>
<namePart type="family">Železný</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ivan</namePart>
<namePart type="family">Gruber</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jakub</namePart>
<namePart type="family">Honzík</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sabyasachi</namePart>
<namePart type="family">Kamila</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Facundo</namePart>
<namePart type="given">Manuel</namePart>
<namePart type="family">Quiroga</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marek</namePart>
<namePart type="family">Hrúz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammed</namePart>
<namePart type="family">Hasanuzzaman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ashutosh</namePart>
<namePart type="family">Modi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-488-0</identifier>
</relatedItem>
<abstract>Uruguayan Sign Language (LSU) is a low-resource language with a lack of computational models. Building on the first gloss-annotated corpus of LSU interpreter videos, we release LSU-446, a curated dataset of 17,793 sign instances over 446 glosses. Unlike standard benchmarks recorded under controlled conditions, LSU-446 consists of interpreted discourse, with an average sign duration of 403 ms, and heavy coarticulation. To the best of our knowledge, this work establishes the first isolated sign recognition baselines for LSU at scale, comparing a Transformer (SPOTER) and a graph convolutional network (MSG3D); MSG3D reaches 32.7% Top-1 over 446 classes and, unlike SPOTER, degrades only marginally as the vocabulary grows. We then use both architectures for a sign spotting experiment on a held-out video from an unseen signer and domain (33.8% out-of-vocabulary), comparing a segmenter-based pipeline against a sliding window, under an evaluation protocol that decouples temporal detection from lexical recognition.</abstract>
<identifier type="citekey">rosa-etal-2026-recognizing</identifier>
<location>
<url>https://aclanthology.org/2026.wslp-1.11/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>111</start>
<end>120</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Recognizing Uruguayan Sign Language Glosses in Continuous Discourse: A Curated Dataset and First Baselines
%A Rosá, Manu Bervejillo
%A Techera, Facundo
%A Lisboa, Emilio Valle
%A Stassi, Ariel E.
%A Chiruzzo, Luis
%Y Singh, Sanjeet
%Y Joshi, Abhinav
%Y Dattatreya, Pokala
%Y Artiaga, Keren
%Y Pujara, Khushi Jaydeep
%Y Železný, Tomáš
%Y Gruber, Ivan
%Y Honzík, Jakub
%Y Kamila, Sabyasachi
%Y Quiroga, Facundo Manuel
%Y Hrúz, Marek
%Y Hasanuzzaman, Mohammed
%Y Modi, Ashutosh
%S Proceedings of the Second Workshop on Sign Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-488-0
%F rosa-etal-2026-recognizing
%X Uruguayan Sign Language (LSU) is a low-resource language with a lack of computational models. Building on the first gloss-annotated corpus of LSU interpreter videos, we release LSU-446, a curated dataset of 17,793 sign instances over 446 glosses. Unlike standard benchmarks recorded under controlled conditions, LSU-446 consists of interpreted discourse, with an average sign duration of 403 ms, and heavy coarticulation. To the best of our knowledge, this work establishes the first isolated sign recognition baselines for LSU at scale, comparing a Transformer (SPOTER) and a graph convolutional network (MSG3D); MSG3D reaches 32.7% Top-1 over 446 classes and, unlike SPOTER, degrades only marginally as the vocabulary grows. We then use both architectures for a sign spotting experiment on a held-out video from an unseen signer and domain (33.8% out-of-vocabulary), comparing a segmenter-based pipeline against a sliding window, under an evaluation protocol that decouples temporal detection from lexical recognition.
%U https://aclanthology.org/2026.wslp-1.11/
%P 111-120
Markdown (Informal)
[Recognizing Uruguayan Sign Language Glosses in Continuous Discourse: A Curated Dataset and First Baselines](https://aclanthology.org/2026.wslp-1.11/) (Rosá et al., WSLP 2026)
ACL