SBU发现:模型解释比喻语言

Yash Kumar Lal, Mohaddeseh Bastan
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引用次数: 1

摘要

比喻语言在人类交际中无处不在。然而,目前的NLP模型无法证明对这种现象的实例有重要的理解。EMNLP 2022关于比喻语言理解的共享任务提出了预测和解释包含比喻语言使用实例的前提和假设之间关系的问题。我们对这个任务使用T5-large的不同变体进行了实验,并构建了一个显著优于任务基线的模型。将其作为T5的新任务,并简单地对数据进行微调,可以在定义的评估中获得最佳分数。此外,我们发现只有假设的模型能够实现大部分的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SBU Figures It Out: Models Explain Figurative Language
Figurative language is ubiquitous in human communication. However, current NLP models are unable to demonstrate a significant understanding of instances of this phenomena. The EMNLP 2022 shared task on figurative language understanding posed the problem of predicting and explaining the relation between a premise and a hypothesis containing an instance of the use of figurative language. We experiment with different variations of using T5-large for this task and build a model that significantly outperforms the task baseline. Treating it as a new task for T5 and simply finetuning on the data achieves the best score on the defined evaluation. Furthermore, we find that hypothesis-only models are able to achieve most of the performance.
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