UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation

Michael Ogezi, B. Hauer, Talgat Omarov, Ning Shi, Grzegorz Kondrak
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引用次数: 1

Abstract

We describe the systems of the University of Alberta team for the SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders. Furthermore, we compare language-specific encoders against the application of English encoders to translated texts. As the contexts given in the task datasets are extremely short, we also experiment with augmenting these contexts with descriptions generated by a language model. This yields substantial improvements in accuracy. We describe and evaluate additional V-WSD methods which use image generation and text-conditioned image segmentation. Some of our experimental results exceed those of our official submissions on the test set. Our code is publicly available at https://github.com/UAlberta-NLP/v-wsd.
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任务1:多语言视觉词义消歧的语境增强与翻译
我们描述了阿尔伯塔大学团队用于SemEval-2023视觉词义消歧(V-WSD)任务的系统。我们提出了一种新的算法,利用从BabelNet检索的光泽,结合文本和图像编码器。此外,我们比较了语言特定编码器与英语编码器在翻译文本中的应用。由于任务数据集中给出的上下文非常短,我们还尝试用语言模型生成的描述来扩展这些上下文。这大大提高了准确性。我们描述和评估了其他使用图像生成和文本条件图像分割的V-WSD方法。我们的一些实验结果超过了我们在测试集上正式提交的结果。我们的代码可以在https://github.com/UAlberta-NLP/v-wsd上公开获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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