Does My Rebuttal Matter? Insights from a Major NLP Conference

22156

Authors

nan

Abstract

AbstractPeer review is a core element of the scientific process, particularly in conference-centered fields such as ML and NLP. However, only few studies have evaluated its properties empirically. Aiming to fill this gap, we present a corpus that contains over 4k reviews and 1.2k author responses from ACL-2018. We quantitatively and qualitatively assess the corpus. This includes a pilot study on paper weaknesses given by reviewers and on quality of author responses. We then focus on the role of the rebuttal phase, and propose a novel task to predict after-rebuttal (i.e., final) scores from initial reviews and author responses. Although author responses do have a marginal (and statistically significant) influence on the final scores, especially for borderline papers, our results suggest that a reviewer’s final score is largely determined by her initial score and the distance to the other reviewers’ initial scores. In this context, we discuss the conformity bias inherent to peer reviewing, a bias that has largely been overlooked in previous research. We hope our analyses will help better assess the usefulness of the rebuttal phase in NLP conferences.

Keywords

nan

Full Content

Additional Notes Original paper Yang Gao, Steffen Eger, Ilia Kuznetsov, Iryna Gurevych, and Yusuke Miyao. "Does My Rebuttal Matter? Insights from a Major NLP Conference." In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1274-1290. 2019. RPB in Figure 2 Yuyal Pinter found that the abbreviation “RPB” in Figure 2 was not defined. RPB stands for replicability. It is an aspect to rate in the review template of ACL 2018. Peer pressure Nihar Shah pointed out a problem with the term “peer pressure” and aspects of correlation/causality in our paper, which indeed allow room for interpretation and might be misleading to some degree. We make the following clarifications. The paper uses the term “peer pressure” to refer to the incentive to update the scores and reach a consensus. Such consensus is often explicitly encouraged by the area chairs, e.g. by enforcing a discussion for submissions with high deviation in scores while leaving submissions with low deviation intact. In our analysis, we use the data from the ACL 2018 conference to estimate which factor is more responsible for the score updates, the authors’ rebuttal or the opinions of the other reviewers, expressed numerically as scores. For this, we conduct regression analyses. If the scores given by the other reviewers dominate in the regression, we take this as evidence for the peer pressure effect; if factors relating to the rebuttal dominate, we assume the peer pressure effect to be weaker. Our experiments in Section 4.3 show that peer pressure dominates the regression. Note that both the texts and the scores of the peer reviews can yield peer pressure. A reviewer can be influenced by the ideas expressed in the other reviewers’ texts, or simply by the pressure to be aligned with the other reviewers in terms of scores. The latter kind of influence is also known as herd behavior (Banerjee, 1992) or conformity bias (Buechelet al., 2015). In our study, we do not distinguish the influence of these two factors, but instead treat the influence from the other peer reviews as a whole as “peer pressure”. Completely disentangling the causes of peer pressure would require controlled experimentation, e.g. allowing a group of reviewers to only see the scores while allowing another group to only see the comments, and analyze the score-update behavior differences in each group. Such experiments are to be set up in advance and integrated into the reviewing process itself and hence are beyond the scope of our study which operates on historical data. We encourage the community to perform such controlled experiments in the future. References Abhijit V. Banerjee. "A simple model of herd behavior." The Quarterly Journal of Economics 107, no. 3 (1992): 797-817. Berno Buechel, Tim Hellmann, and Stefan Klößner. "Opinion dynamics and wisdom under conformity." Journal of Economic Dynamics and Control 52 (2015): 240-257.

Labeled Text

AbstractPeer review is a core element of the scientific process, particularly in conference-centered fields such as ML and NLP. However, only few studies have evaluated its properties empirically. Aiming to fill this gap, we present a corpus that contains over 4k reviews and 1.2k author responses from ACL-2018. We quantitatively and qualitatively assess the corpus. This includes a pilot study on paper weaknesses given by reviewers and on quality of author responses. We then focus on the role of the rebuttal phase, and propose a novel task to predict after-rebuttal (i.e., final) scores from initial reviews and author responses. Although author responses do have a marginal (and statistically significant) influence on the final scores, especially for borderline papers, our results suggest that a reviewer’s final score is largely determined by her initial score and the distance to the other reviewers’ initial scores. In this context, we discuss the conformity bias inherent to peer reviewing, a bias that has largely been overlooked in previous research. We hope our analyses will help better assess the usefulness of the rebuttal phase in NLP conferences.

Publication Details

Paper ID:
22156
Year:
2019
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan