Paper meta data correction for 2023.banglalp-1.27, closes #2911.

data/xml/2023.banglalp.xml

@@ -330,8 +330,8 @@
     <paper id="27">
       <title><fixed-case>UFAL</fixed-case>-<fixed-case>ULD</fixed-case> at <fixed-case>BLP</fixed-case>-2023 Task 1: Violence Detection in <fixed-case>B</fixed-case>angla Text</title>
       <author><first>Sourabrata</first><last>Mukherjee</last></author>
-      <author><first>Atul</first><last>Ojha</last><affiliation>University of Galway, Ireland, Insight SFI Research Centre for Data Analytics, DSI, University of Galway, Ireland and Panlingua Languague Processing LLP, India</affiliation></author>
-      <author><first>Ondrej</first><last>Dusek</last><affiliation>Charles University, Prague</affiliation></author>
+      <author><first>Atul Kr.</first><last>Ojha</last><affiliation>University of Galway, Ireland, Insight SFI Research Centre for Data Analytics, DSI, University of Galway, Ireland and Panlingua Languague Processing LLP, India</affiliation></author>
+      <author><first>Ondřej</first><last>Dušek</last><affiliation>Charles University, Prague</affiliation></author>
       <pages>220-224</pages>
       <abstract>In this paper, we present UFAL-ULD team’s system, desinged as a part of the BLP Shared Task 1: Violence Inciting Text Detection (VITD). This task aims to classify text, with a particular challenge of identifying incitement to violence into Direct, Indirect or Non-violence levels. We experimented with several pre-trained sequence classification models, including XLM-RoBERTa, BanglaBERT, Bangla BERT Base, and Multilingual BERT. Our best-performing model was based on the XLM-RoBERTa-base architecture, which outperformed the baseline models. Our system was ranked 20th among the 27 teams that participated in the task.</abstract>
       <url hash="ef906da5">2023.banglalp-1.27</url>