What Makes a Scientific Paper be Accepted for Publication? (2021)

Abstract: Despite peer-reviewing being an essential component of academia since the 1600s, it has repeatedly received criticisms for lack of transparency and consistency. We posit that recent work in machine learning and explainable AI provide tools that enable insights into the decisions from a given peer-review process. We start by simulating the peer-review process using an ML classifier and extracting global explanations in the form of linguistic features that affect the acceptance of a scientific paper for publication on an open peer-review dataset. Second, since such global explanations do not justify causal interpretations, we propose a methodology for detecting confounding effects in natural language and generating explanations, disentangled from textual confounders, in the form of lexicons. Our proposed linguistic explanation methodology indicates the following on a case dataset of ICLR submissions: a) the organising committee follows, for the most part, the recommendations of reviewers, and b) the paper’s main characteristics that led to reviewers recommending acceptance for publication are originality, clarity and substance.

## introduction the peer review process has been instrumental in academia for determining which papers meet the quality standards for publication in scientific journals and conferences. however, it has received several criticisms, including inconsistencies among review texts, review scores, and the final acceptance decision @xcite , arbitrariness between different reviewer groups @xcite as well as reviewer bias in "singleblind" peer reviews @xcite . explainable ai (xai) techniques have been shown to provide global understanding of the data behind a model. 1 assume we build a classifier that 1 among the several definitions for explainability and inter-predicts whether a paper is accepted for publication based on a peer review. interpreting the decision of such a classifier can help comprehend what the reviewers value the most on a research paper. xai allows us to elicit knowledge from the model about the data @xcite . however, in order to determine what aspects of a paper lead to its acceptance, we need to interpret the peer review classifier explanations causally. generally, xai methods do not provide such guarantees since most machine learning models simply detect correlations @xcite . nevertheless, in recent years there has been a trend towards enabling machine learning to adjust for causal inference @xcite . simply interpreting a non-causal classifier does not suffice when attempting to gain insight into human decision-making in the peer review process. for instance, words such as "gan", and "efficiency" appear to be important in such a classifier (section 4). we argue that correlation between such words and paper acceptance is confounded on the subject of the paper: different subjects have different probabilities of acceptance, as well as different degrees to which algorithmic "efficiency" is an important factor. since paper subject ground truth is not necessarily available, we can treat the abstract of a paper as a proxy for its subject, similar to @xcite . previous approaches that aim to reconcile nlp with causal inference are limited to either binary treatment variables @xcite @xcite or nominal confounders @xcite . this paper takes a first step towards using learnt natural language embeddings both as the treatment and the confounder. we achieve this by generating deconfounded lexicons @xcite through pretability @xcite @xcite , we follow the definition of explainability as a plausible justification for the prediction of a model @xcite , and use the terms interpretability and explainability interchangeably. in experiments with a dataset from iclr 2017, we found that the organising committee mostly followed the recommendations of the reviewers, and that the reviewers focused on aspects such as originality, clarity, impact, and soundness of a paper, when suggesting whether or not the paper should be accepted for publication. the paper is organised as follows: section 2 presents related work regarding computational analyses of the peer-review process and explainability in nlp. section 3 gives an overview of peerread, the peer review dataset we used in this paper. section 4 presents our initial exploration on explanation of peer-reviewing, while section 5 describes our methodology to account for causality by generating deconfounded lexicons. ## dataset the peerread dataset @xcite consists of 14.7k papers and 10.7k peer reviews, including the meta-reviews by the editor. the papers are from different venues, having been collected with different methods, which leads to a non-uniform dataset. for instance, arxiv papers do not con-tain any reviews, and consequently, they are out of scope for our study. furthermore, the neurips section contains 2k reviews for only accepted papers. therefore, using them will lead to a significant imbalance in our data. for those reasons, we decided to examine the 1.3k iclr 2017 reviews of peerread. the iclr 2017 section is divided into training, validation and test sets with an 80%-10%-10% split. in the appendix, we can observe the proportion of the accepted to rejected paper in the various partitions of iclr 2017. peerread suffers from various data quality issues, which we have resolved. firstly, we have removed several empty and duplicate reviews. more importantly, for iclr 2017 the meta-reviews which contain the review and final decision of the conference chair are not marked as meta-reviews (the meta-review boolean field is marked as false) as they should have been @xcite . instead, they are all marked as normal reviews with the title "iclr committee final decision". we explicitly treat them as meta-reviews, a differentiation that we believe is crucial in an xai study like ours. notably, subsequent studies that utilise peer-read, like deepsentipeer @xcite , do not mention whether they have addressed this issue. this hinders a direct comparison with results from deepsentipeer as, expectedly, according to our experiments, the use of the meta-reviews significantly increases the performance of classifiers. ## data exploration with global xai we make an initial exploration into global explanations in the form of lexicons by mapping each word to an aggregate of scores corresponding to local explanations of peerread test sample predictions, as suggested both by the creators of shap @xcite and by @xcite . we follow a train-test split setup in our modelling experiments, and only extract explanations from the test set, following @xcite . we experimented with three different classification tasks: a) the final decision peer review classifier fuses the multiple peer reviews to predict the final acceptance decision for a paper, b) the meta-review classifier uses the singular meta-review to predict the final acceptance decision for a paper, and c) the individual peer review classifier predicts the recommendation of the reviewer for a single review, where we consider as accepted the papers with scores above 5, given a range of 1-10. ## producing causal explanations therefore, the correlation of the novelty and the acceptance of a paper may be an artefact of this confounding effect on the subject of the paper. adjusting for every possible confounding and mediating effect is not possible for most machine learning models, partially due to lack of ground truth data. causality in machine learning is limited to taking several strong assumptions and providing a causal interpretation, for as long as those assumptions hold @xcite . when text is involved, even more assumptions must be made, due to its high dimensionality @xcite . we assume that: a) the subject of a paper is sufficient to identify the causal effect, b) the abstract can act as a proxy to the subject, and c) the embedding method extracts information relevant to the subject. in section 5.4, we discuss the validity and limitations of those assumptions. even when such assumptions are violated, our methodology is meaningful since it allows disentangling explanations of text classifiers from the natural language embedding of the text we want to control. ## conclusions with peer-reviewing as a use case, we have proposed a framework for interpreting the decisions of text classifiers causally, by using natural language to adjust for confounding effects. our methodology succeeds in extracting deconfounded lexicons that exclude terms related to the paper subject (i.e., the confounders), a task where established, noncausal global explanation approaches fail. prior work was limited to either nominal confounders @xcite or binary treatment variables @xcite . furthermore, we have extended the deep residualisation @xcite to allow for black-box models. as is standard in causal inference @xcite , the causal guarantees of the deconfounded lexicon we generate is limited by certain assumptions. future work could explore strengthening our assumptions through fine-tuning of the transformer language model representation of the confounder text. furthermore, fine-tuning the encoder of dr+lime may lead to producing more informative lexicons. to avoid overfitting in this finetuning process, we intend to apply our methodology on larger datasets that the one used in this paper for peer reviews.