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- W4361806031 abstract "Despite its crucial role in research experiments, code correctness is often presumed only on the basis of the perceived quality of results. This assumption comes with the risk of erroneous outcomes and potentially misleading findings. To address this issue, we posit that the current focus on reproducibility should go hand in hand with the emphasis on software quality. We present a case study in which we identify and fix three bugs in widely used implementations of the state-of-the-art Conformer architecture. Through experiments on speech recognition and translation in various languages, we demonstrate that the presence of bugs does not prevent the achievement of good and reproducible results, which however can lead to incorrect conclusions that potentially misguide future research. As a countermeasure, we propose a Code-quality Checklist and release pangoliNN, a library dedicated to testing neural models, with the goal of promoting coding best practices and improving research software quality within the NLP community." @default.
- W4361806031 created "2023-04-05" @default.
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- W4361806031 date "2023-03-28" @default.
- W4361806031 modified "2023-10-18" @default.
- W4361806031 title "When Good and Reproducible Results are a Giant with Feet of Clay: The Importance of Software Quality in NLP" @default.
- W4361806031 doi "https://doi.org/10.48550/arxiv.2303.16166" @default.
- W4361806031 hasPublicationYear "2023" @default.
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