From 11bd47ca7449be7670db396b2de4043cf936a9c4 Mon Sep 17 00:00:00 2001
From: HAMIDULLAH Yasser <13780954+yhamidullah@users.noreply.github.com>
Date: Thu, 15 Aug 2024 22:15:09 +0200
Subject: [PATCH] Update 2024.acl.xml

Corrections:
*Name and First name inverted: Yasser Hamidullah
*"van"  missing in Josef van Genabith
---
 data/xml/2024.acl.xml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/data/xml/2024.acl.xml b/data/xml/2024.acl.xml
index d1959f9df6..3a7684192f 100644
--- a/data/xml/2024.acl.xml
+++ b/data/xml/2024.acl.xml
@@ -10446,13 +10446,13 @@
     </paper>
     <paper id="40">
       <title>Sign Language Translation with Sentence Embedding Supervision</title>
-      <author><first>Hamidullah</first><last>Yasser</last></author>
-      <author><first>Josef</first><last>Genabith</last><affiliation>German Research Center for AI and Universität des Saarlandes</affiliation></author>
+      <author><first>Yasser</first><last>Hamidullah</last></author>
+      <author><first>Josef</first><last>van Genabith</last><affiliation>German Research Center for AI and Universität des Saarlandes</affiliation></author>
       <author><first>Cristina</first><last>España-Bonet</last><affiliation>German Research Center for AI</affiliation></author>
       <pages>425-434</pages>
       <abstract>State-of-the-art sign language translation (SLT) systems facilitate the learning process through gloss annotations, either in an end2end manner or by involving an intermediate step. Unfortunately, gloss labelled sign language data is usually not available at scale and, when available, gloss annotations widely differ from dataset to dataset. We present a novel approach using sentence embeddings of the target sentences at training time that take the role of glosses. The new kind of supervision does not need any manual annotation but it is learned on raw textual data. As our approach easily facilitates multilinguality, we evaluate it on datasets covering German (PHOENIX-2014T) and American (How2Sign) sign languages and experiment with mono- and multilingual sentence embeddings and translation systems. Our approach significantly outperforms other gloss-free approaches, setting the new state-of-the-art for data sets where glosses are not available and when no additional SLT datasets are used for pretraining, diminishing the gap between gloss-free and gloss-dependent systems.</abstract>
       <url hash="237e48a7">2024.acl-short.40</url>
-      <bibkey>yasser-etal-2024-sign</bibkey>
+      <bibkey>hamidullah-etal-2024-sign</bibkey>
     </paper>
     <paper id="41">
       <title><fixed-case>STREAM</fixed-case>: Simplified Topic Retrieval, Exploration, and Analysis Module</title>