Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graph

• URL: http://arxiv.org/abs/2305.12900v2
• Date: Sun, 11 Jun 2023 08:23:23 GMT
• Title: Evaluating Prompt-based Question Answering for Object Prediction in the Open Research Knowledge Graph
• Authors: Jennifer D'Souza, Moussab Hrou and S\"oren Auer
• Abstract summary: This work reports results on adopting prompt-based training of transformers for textitscholarly knowledge graph object prediction It deviates from the other works proposing entity and relation extraction pipelines for predicting objects of a scholarly knowledge graph. We find that (i) per expectations, transformer models when tested out-of-the-box underperform on a new domain of data, (ii) prompt-based training of the models achieve performance boosts of up to 40% in a relaxed evaluation setting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: There have been many recent investigations into prompt-based training of transformer language models for new text genres in low-resource settings. The prompt-based training approach has been found to be effective in generalizing pre-trained or fine-tuned models for transfer to resource-scarce settings. This work, for the first time, reports results on adopting prompt-based training of transformers for \textit{scholarly knowledge graph object prediction}. The work is unique in the following two main aspects. 1) It deviates from the other works proposing entity and relation extraction pipelines for predicting objects of a scholarly knowledge graph. 2) While other works have tested the method on text genera relatively close to the general knowledge domain, we test the method for a significantly different domain, i.e. scholarly knowledge, in turn testing the linguistic, probabilistic, and factual generalizability of these large-scale transformer models. We find that (i) per expectations, transformer models when tested out-of-the-box underperform on a new domain of data, (ii) prompt-based training of the models achieve performance boosts of up to 40\% in a relaxed evaluation setting, and (iii) testing the models on a starkly different domain even with a clever training objective in a low resource setting makes evident the domain knowledge capture gap offering an empirically-verified incentive for investing more attention and resources to the scholarly domain in the context of transformer models.

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