  Exponential growth in digital information outlets and the race to publish has
made scientific misinformation more prevalent than ever. However, the task to
fact-verify a given scientific claim is not straightforward even for
researchers. Scientific claim verification requires in-depth knowledge and
great labor from domain experts to substantiate supporting and refuting
evidence from credible scientific sources. The SciFact dataset and
corresponding task provide a benchmarking leaderboard to the community to
develop automatic scientific claim verification systems via extracting and
assimilating relevant evidence rationales from source abstracts. In this work,
we propose a modular approach that sequentially carries out binary
classification for every prediction subtask as in the SciFact leaderboard. Our
simple classifier-based approach uses reduced abstract representations to
retrieve relevant abstracts. These are further used to train the relevant
rationale-selection model. Finally, we carry out two-step stance predictions
that first differentiate non-relevant rationales and then identify supporting
or refuting rationales for a given claim. Experimentally, our system RerrFact
with no fine-tuning, simple design, and a fraction of model parameters fairs
competitively on the leaderboard against large-scale, modular, and joint
modeling approaches. We make our codebase available at
https://github.com/ashishrana160796/RerrFact.
