تقرير
Scientific and Creative Analogies in Pretrained Language Models
| العنوان: | Scientific and Creative Analogies in Pretrained Language Models |
|---|---|
| المؤلفون: | Czinczoll, Tamara, Yannakoudakis, Helen, Mishra, Pushkar, Shutova, Ekaterina |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Machine Learning |
| الوصف: | This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2. Existing analogy datasets typically focus on a limited set of analogical relations, with a high similarity of the two domains between which the analogy holds. As a more realistic setup, we introduce the Scientific and Creative Analogy dataset (SCAN), a novel analogy dataset containing systematic mappings of multiple attributes and relational structures across dissimilar domains. Using this dataset, we test the analogical reasoning capabilities of several widely-used pretrained language models (LMs). We find that state-of-the-art LMs achieve low performance on these complex analogy tasks, highlighting the challenges still posed by analogy understanding. Comment: To be published in Findings of EMNLP 2022 |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2211.15268Test |
| رقم الانضمام: | edsarx.2211.15268 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |