Scientific Claim Verification with Evidence from Text and Structured Knowledge

The research project VeriSci focuses on developing natural language processing (NLP) solutions for automated evaluation and assessment of scientific claims. The holistic process of fact verification involves detecting check-worthy claims, finding relevant documents in a corpus of articles, extracting passages containing appropriate evidence, and finally making a decision on the veracity of the claim by inferring if there is logical entailment between the claim and found evidence. To achieve these goals, the models based on different architectures will be investigated and evaluated. To increase trust in the developed machine-learning model, the mechanisms for making their decisions process interpretable and explainable to humans will be explored. In addition to the theoretical contributions of the project, a prototype system for real-time scientific claim verification will be constructed.


Owing to the complexity of language used in scientific publications, language models trained on general-purpose data can struggle with scientific text. Therefore, methods for domain adaption to scientific text will be explored. Due to the highly hierarchical nature of scientific knowledge, knowledge graphs and ontologies are commonly used to represent scientific concepts and relations between them in a structured format. These structured-knowledge resources will be explored in the project concerning their usefulness in augmenting the performance of language models. The project will also look at related tasks of natural language understanding (NLU) in the scientific domain, such as question answering, argumentation mining, and natural language inference, as well as tackling the problem of the factual correctness of automatically generated text summaries.