diff --git a/data/xml/2023.findings.xml b/data/xml/2023.findings.xml
index 9b44fa1e2f..12d4f7ef56 100644
--- a/data/xml/2023.findings.xml
+++ b/data/xml/2023.findings.xml
@@ -15466,16 +15466,6 @@
       <bibkey>xie-etal-2023-mixtea</bibkey>
       <doi>10.18653/v1/2023.findings-emnlp.63</doi>
     </paper>
-    <paper id="64">
-      <title><fixed-case>EZ</fixed-case>-<fixed-case>STANCE</fixed-case>: A Large Dataset for Zero-Shot Stance Detection</title>
-      <author><first>Chenye</first><last>Zhao</last></author>
-      <author><first>Cornelia</first><last>Caragea</last></author>
-      <pages>897-911</pages>
-      <abstract>Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in favor of, against, or neutral toward a target that is unseen during training. In this paper, we present EZ-STANCE, a large English ZSSD dataset with 30,606 annotated text-target pairs. In contrast to VAST, the only other existing ZSSD dataset, EZ-STANCE includes both noun-phrase targets and claim targets, covering a wide range of domains. In addition, we introduce two challenging subtasks for ZSSD: target-based ZSSD and domain-based ZSSD. We provide an in-depth description and analysis of our dataset. We evaluate EZ-STANCE using state-of-the-art deep learning models. Furthermore, we propose to transform ZSSD into the NLI task by applying two simple yet effective prompts to noun-phrase targets. Our experimental results show that EZ-STANCE is a challenging new benchmark, which provides significant research opportunities on ZSSD. We will make our dataset and code available on GitHub.</abstract>
-      <url hash="c64be5b5">2023.findings-emnlp.64</url>
-      <bibkey>zhao-caragea-2023-ez</bibkey>
-      <doi>10.18653/v1/2023.findings-emnlp.64</doi>
-    </paper>
     <paper id="65">
       <title>Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval</title>
       <author><first>Fan</first><last>Jiang</last></author>