| Challenge: | Neural network models are usually very data-hungry and performance of such models can suffer when labeled data is not available. |
| Approach: | They propose to provide models with additional analogy sources to strengthen analogy-formation . they propose to combine the analogy motivated approach with data hallucination or augmentation . |
| Outcome: | The proposed methods improve on state-of-the-art results on 46 languages, especially in low-resource settings. |
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| Challenge: | a computational model of word borrowing can be useful for lexicon expansion and language preservation. |
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Scientific and Creative Analogies in Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing analogy datasets focus on a limited set of analogical relations with a high similarity of the two domains between which the analogy holds. |
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Can language models learn analogical reasoning? Investigating training objectives and comparisons to human performance (2023.emnlp-main)
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| Challenge: | Analogical reasoning is a common way to evaluate word embeddings in NLP, but it is also of interest to investigate whether or not it is able to be learned. |
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Interpreting Predictions of NLP Models (2020.emnlp-tutorials)
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Multilingual Culture-Independent Word Analogy Datasets (2020.lrec-1)
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| Challenge: | In text processing, deep neural networks use word embeddings as an input. |
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Nianqi Li, Siyu Yuan, Jiangjie Chen, Jiaqing Liang, Feng Wei, Zujie Liang, Deqing Yang, Yanghua Xiao
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Implicit Representations of Meaning in Neural Language Models (2021.acl-long)
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| Challenge: | Neural language models (NLMs) encode lexical relations and syntactic structure, but their effectiveness is still unclear. |
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Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations (C18-1)
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BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies? (2021.acl-long)
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| Challenge: | Analogies play a central role in human commonsense reasoning. |
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On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models (2024.findings-eacl)
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Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Analogies facilitate the transfer of meaning and knowledge from one domain to another. |
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