Metadata correction for 2021.eacl-tutorials.3 (acl-org#1968)

data/xml/2021.eacl.xml

@@ -5323,13 +5323,13 @@
       <doi>10.18653/v1/2021.eacl-tutorials.2</doi>
     </paper>
     <paper id="3">
-      <title>Tutorial Proposal: End-to-End Speech Translation</title>
+      <title>Tutorial: End-to-End Speech Translation</title>
       <author><first>Jan</first><last>Niehues</last></author>
       <author><first>Elizabeth</first><last>Salesky</last></author>
       <author><first>Marco</first><last>Turchi</last></author>
       <author><first>Matteo</first><last>Negri</last></author>
       <pages>10–13</pages>
-      <abstract>Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. Speech translation has attracted interest for many years, but the recent successful applications of deep learning to both individual tasks have enabled new opportunities through joint modeling, in what we today call ‘end-to-end speech translation.’ In this tutorial we will introduce the techniques used in cutting-edge research on speech translation. Starting from the traditional cascaded approach, we will given an overview on data sources and model architectures to achieve state-of-the art performance with end-to-end speech translation for both high- and low-resource languages. In addition, we will discuss methods to evaluate analyze the proposed solutions, as well as the challenges faced when applying speech translation models for real-world applications.</abstract>
+      <abstract>Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. Speech translation has attracted interest for many years, but the recent successful applications of deep learning to both individual tasks have enabled new opportunities through joint modeling, in what we today call ‘end-to-end speech translation.’ In this tutorial we introduce the techniques used in cutting-edge research on speech translation. Starting from the traditional cascaded approach, we give an overview on data sources and model architectures to achieve state-of-the art performance with end-to-end speech translation for both high- and low-resource languages. In addition, we discuss methods to evaluate analyze the proposed solutions, as well as the challenges faced when applying speech translation models for real-world applications.</abstract>
       <url hash="29e48e66">2021.eacl-tutorials.3</url>
       <bibkey>niehues-etal-2021-tutorial</bibkey>
       <doi>10.18653/v1/2021.eacl-tutorials.3</doi>