Arkadiy Saakyan, Tuhin Chakrabarty, Debanjan Ghosh, S. Muresan
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引用次数: 2
摘要
我们介绍了我们在EMNLP 2022第三届比喻语言处理研讨会(FigLang 2022)上进行的关于理解比喻语言的共享任务的结果。共享任务基于FLUTE数据集(Chakrabarty et al.， 2022)，该数据集由包含比喻语言的NLI对以及每个NLI实例的免费文本解释组成。这项任务要求参与者建立模型，不仅能够预测比喻性NLI实例的正确标签，而且还能生成令人信服的自由文本解释。参与者能够在自动和人工评估设置中显著改善提供的基线。我们进一步总结了提交的系统，并讨论了评估结果。
A Report on the FigLang 2022 Shared Task on Understanding Figurative Language
We present the results of the Shared Task on Understanding Figurative Language that we conducted as a part of the 3rd Workshop on Figurative Language Processing (FigLang 2022) at EMNLP 2022. The shared task is based on the FLUTE dataset (Chakrabarty et al., 2022), which consists of NLI pairs containing figurative language along with free text explanations for each NLI instance. The task challenged participants to build models that are able to not only predict the right label for a figurative NLI instance, but also generate a convincing free-text explanation. The participants were able to significantly improve upon provided baselines in both automatic and human evaluation settings. We further summarize the submitted systems and discuss the evaluation results.
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