Fact or Fiction: Verifying Scientific Claims (2004.14974v6)

Abstract: We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision. To study this task, we construct SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. We develop baseline models for SciFact, and demonstrate that simple domain adaptation techniques substantially improve performance compared to models trained on Wikipedia or political news. We show that our system is able to verify claims related to COVID-19 by identifying evidence from the CORD-19 corpus. Our experiments indicate that SciFact will provide a challenging testbed for the development of new systems designed to retrieve and reason over corpora containing specialized domain knowledge. Data and code for this new task are publicly available at https://github.com/allenai/scifact. A leaderboard and COVID-19 fact-checking demo are available at https://scifact.apps.allenai.org.

Insights on Scientific Claim Verification

The paper "Fact or Fiction: Verifying Scientific Claims" introduces the task of scientific claim verification, addressing the growing complexity and volume of scientific literature which challenges both researchers and the public in evaluating the veracity of scientific findings. The authors propose the SciFact dataset, which is composed of 1.4K scientifically grounded claims paired with annotated abstracts that provide evidence either supporting or refuting the claims. The data is complemented with rationale annotations, offering transparency into the decision-making processes of models developed for this task.

The central contribution lies in leveraging domain adaptation techniques to enhance model performance when transferring from more general datasets, like those involving Wikipedia or political news, to the specialized domain of scientific literature. This adaptation is especially crucial when confronting complex and specialized tasks such as identifying evidence for claims related to emergent topics like COVID-19. The paper demonstrates this capability by effectively employing the CORD-19 corpus for claim validation, underscoring the adaptability and robustness of the proposed approach.

The paper not only provides a structured dataset but also establishes baseline models as a benchmark for future research. By formulating a challenging testbed, it encourages the development of systems capable of sophisticated retrieval and reasoning over large corpora that encompass specialized domain knowledge. The task is well-aligned with pressing needs in the scientific community where rapid verification of claims can significantly aid decision-making, particularly during crises, as exemplified by the ongoing pandemic.

The implications of this research extend to both theoretical and practical domains. Theoretically, the task and dataset encourage advancements in NLP methods that require intricate reasoning capabilities, including understanding scientific nomenclature, experimental settings, and contextual comparisons among findings. Practically, it paves the way for applications in automated systems that support evidence synthesis in scientific research, aiding not just researchers but also policy-makers in assessing evidence-based information.

Future developments could see the integration of such systems into broader frameworks for automated scientific review, enhancing their capacity to filter misinformation or ensure the precision of emergent scientific narratives. Moreover, as further research explores the intricacies of scientific claim verification, we might witness enhancements in model interpretability and precision, vital for deployment in sensitive areas like public health and policy advising. The progression from supporting evidence retrieval to constructing comprehensive systems for nuanced, domain-specific claim verification marks a significant trajectory for NLP in scientific applications.