How does *ACL conferences acceptance work [D]

The recent Reddit thread questioning the acceptance process at Association for Computational Linguistics (ACL) conferences highlights a persistent frustration within the NLP research community: the perceived opacity of peer review. The author's observation—seeing papers with strong meta-reviews still rejected while others with seemingly less favorable feedback are accepted—resonates with many who have navigated the conference submission cycle. It underscores a larger issue regarding the weight given to various review components and the subjective nature of the final decision. This discussion is particularly relevant given the ongoing debate about submission volume and its impact on acceptance rates, as explored in Why doesn't the ML research community limit the number of submissions per author?. The sheer volume of submissions makes the review process inherently challenging, and the desire for greater transparency is understandable.

The core of the question revolves around the relative importance of the meta-review, individual reviewer scores, and the track a paper is submitted to. While ACL venues officially state they consider all aspects of the reviews, the author’s experience suggests a more nuanced reality. It’s likely that the meta-review, acting as a synthesis of reviewer feedback and a final judgment on the paper's merit, carries significant weight. However, ignoring the individual reviews entirely would be a disservice. Conference committees presumably look for patterns and inconsistencies in the reviews, rather than solely relying on a single summary. Furthermore, the track itself—whether it's a main conference track, a workshop, or a late-breaking-news session—establishes a different bar for acceptance, and papers are evaluated accordingly. The concerns raised also echo similar anxieties about the predictive power of human preference, as explored in Predicting human preference for generated image pairs using HPSv3, where the reliability of metrics in gauging true value is questioned.

The process isn't simply about aggregating scores; it involves a qualitative assessment of the paper's contribution, novelty, and clarity. Committees are looking for work that genuinely advances the field, and they may prioritize papers that address critical limitations or offer innovative approaches even if the reviews aren’t uniformly glowing. The seemingly contradictory outcomes – a strong meta-review leading to rejection while a weaker one results in acceptance – likely stem from this qualitative judgment, which is inherently subjective. This subjectivity is further complicated by the fact that different committees may have different priorities and perspectives. A paper deemed valuable by one committee might not be seen as such by another, especially when considering the diverse range of topics and methodologies represented across ACL venues. The recent discussion about withdrawing a paper and resubmitting to a workshop Withdraw from ACL ARR and resubmit to a workshop? illustrates the strategic considerations authors sometimes make in response to initial ARR feedback.

Ultimately, the ACL acceptance process remains a complex and somewhat opaque system. While efforts have been made to improve transparency, the subjective nature of peer review ensures that uncertainty and frustration will persist. As the field of NLP continues to grow and evolve, with an increasing volume of submissions, exploring alternative review models—perhaps incorporating more structured feedback mechanisms or utilizing AI-assisted tools to identify patterns in reviewer comments—could be beneficial. The core question remains: how can we create a more equitable and transparent system that accurately reflects the true value of research contributions while acknowledging the inherent challenges of human judgment and the escalating demands on reviewers?