The anxieties surrounding peer review are universal, and this post from /u/hepiga highlights a particularly common dilemma: navigating initial ARR reviews and strategizing for IJCNLP-AACL acceptance. It's encouraging to see researchers engaging with the ARR system and openly discussing their experiences, particularly as the platform continues to shape the landscape of academic publishing. The concerns raised about score weighting and the impact of rebuttals are valid and resonate with many authors preparing for major conferences. This situation underscores the evolving dynamics of the peer review process, especially within the rapidly advancing field of Multilingualism and Cross-Lingual NLP, where nuanced evaluations can be critical. Related discussions around ARR scores, as seen in ACL ARR May 2026, demonstrate a broader community interest in understanding and optimizing the system. Further, the challenges in evaluating datasets and software, as pointed out by the reviewers, echo concerns raised in other contexts, such as the debate around the practical application of models like those discussed in DINOv2 way worse than SigLIP in k-NN, demonstrating the need for rigorous and consistent assessment criteria.
The scores presented – an average review score of 2.83 and a confidence score of 3.33 – provide a mixed signal. While the reproducibility score of 4 is a strong positive, the variability in excitement scores (2, 3, 3) and the presence of a significantly lower 2.5 review are points of concern. The brevity of the 2.5 review, coupled with the stark contrast in assessments of datasets and software, is particularly puzzling. It suggests a potential disconnect between the reviewer’s understanding of the work and the broader evaluation. It's prudent to acknowledge that outlier reviews can occur, and the ARR system’s weighting mechanisms are crucial in mitigating their disproportionate influence. The detailed (3,3) review, with its thorough list of weaknesses and strengths, provides invaluable feedback that should be carefully considered and addressed in the rebuttal. Addressing writing issues highlighted in the (3,4) review is also paramount, as clarity and effective communication are essential for conveying complex technical concepts.
The key to navigating this situation lies in a thoughtful and strategic rebuttal. Rather than defensively disputing individual scores, focus on demonstrating engagement with the reviewers’ feedback and outlining concrete steps taken to address their concerns. A well-crafted rebuttal should not be perceived as an argument, but as a demonstration of responsiveness and a commitment to improving the paper. ARR’s guidelines on rebuttal format and content should be meticulously followed, ensuring clarity and conciseness. It's reasonable to expect that rebuttals can influence reviewer perspectives, especially when they demonstrate a genuine effort to address identified weaknesses. However, it’s equally important to temper expectations; not every reviewer will be swayed, and scores may not dramatically shift. The focus should remain on strengthening the paper and providing a clear and compelling rationale for its contribution to the field of Multilingualism and Cross-Lingual NLP.
Ultimately, this submission’s journey through ARR highlights the iterative nature of the research process. The challenges encountered are not unique, and the willingness to engage with feedback and refine the work is a hallmark of robust scholarship. The future of academic publishing increasingly relies on platforms like ARR to facilitate open and transparent evaluation. It raises a fundamental question: how can we, as a community, better equip reviewers with the tools and training necessary to provide consistently insightful and constructive feedback, especially in rapidly evolving technological domains? The ongoing discussions around ARR and similar platforms will undoubtedly shape the future of how research is evaluated and disseminated.