From 9aa495d07cdabeb99f58ac7f31b4fee9c730eeed Mon Sep 17 00:00:00 2001 From: Matt Post Date: Wed, 7 Feb 2024 21:45:05 -0500 Subject: [PATCH] Remove mistakenly-published paper (#3077) --- data/xml/2023.findings.xml | 10 ---------- 1 file changed, 10 deletions(-) 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 @@ xie-etal-2023-mixtea 10.18653/v1/2023.findings-emnlp.63 - - <fixed-case>EZ</fixed-case>-<fixed-case>STANCE</fixed-case>: A Large Dataset for Zero-Shot Stance Detection - ChenyeZhao - CorneliaCaragea - 897-911 - 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. - 2023.findings-emnlp.64 - zhao-caragea-2023-ez - 10.18653/v1/2023.findings-emnlp.64 - Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval FanJiang