- Conference Paper
Rights / licenseCreative Commons Attribution 4.0 International
Knowledge bases (KBs) are essential for many downstream NLP tasks, yet their prime shortcoming is that they are often incomplete. State-of-the-art frameworks for KB completion often lack sufficient accuracy to work fully automated without human supervision. As a remedy, we propose : a novel interactive framework for KB completion from text based on a question answering pipeline. Our framework is tailored to the specific needs of a human-in-the-loop paradigm: (i) We generate facts that are aligned with text snippets and are thus immediately verifiable by humans. (ii) Our system is designed such that it continuously learns during the KB completion task and, therefore, significantly improves its performance upon initial zero- and few-shot relations over time. (iii) We only trigger human interactions when there is enough information for a correct prediction. Therefore, we train our system with negative examples and a fold-option if there is no answer. Our framework yields a favorable performance: it achieves a hit@1 ratio of 29.7% for initially unseen relations, upon which it gradually improves to 46.2%. Show more
Book titleProceedings of the 28th International Conference on Computational Linguistics
Pages / Article No.
PublisherInternational Committee on Computational Linguistics
Organisational unit09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
NotesDue to the Corona virus (COVID-19) the conference was conducted virtually.
MoreShow all metadata