Does My Rebuttal Matter? Insights from a Major NLP Conference

Peer 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.Comment: Accepted to NAACL-HLT 2019. Main paper plus supplementary materia

The Open Review-Based (ORB) dataset: Towards Automatic Assessment of Scientific Papers and Experiment Proposals in High-Energy Physics

With the Open Science approach becoming important for research, the evolution towards open scientific-paper reviews is making an impact on the scientific community. However, there is a lack of publicly available resources for conducting research activities related to this subject, as only a limited number of journals and conferences currently allow access to their review process for interested parties. In this paper, we introduce the new comprehensive Open Review-Based dataset (ORB); it includes a curated list of more than 36,000 scientific papers with their more than 89,000 reviews and final decisions. We gather this information from two sources: the OpenReview.net and SciPost.org websites. However, given the volatile nature of this domain, the software infrastructure that we introduce to supplement the ORB dataset is designed to accommodate additional resources in the future. The ORB deliverables include (1) Python code (interfaces and implementations) to translate document data and metadata into a structured and high-level representation, (2) an ETL process (Extract, Transform, Load) to facilitate the automatic updates from defined sources and (3) data files representing the structured data. The paper presents our data architecture and an overview of the collected data along with relevant statistics. For illustration purposes, we also discuss preliminary Natural-Language-Processing-based experiments that aim to predict (1) papers' acceptance based on their textual embeddings, and (2) grading statistics inferred from embeddings as well. We believe ORB provides a valuable resource for researchers interested in open science and review, with our implementation easing the use of this data for further analysis and experimentation. We plan to update ORB as the field matures as well as introduce new resources even more fitted to dedicated scientific domains such as High-Energy Physics.Comment: 13 pages, supplementary material included, dataset availabl

Aspect-based Sentiment Analysis of Scientific Reviews

Scientific papers are complex and understanding the usefulness of these papers requires prior knowledge. Peer reviews are comments on a paper provided by designated experts on that field and hold a substantial amount of information, not only for the editors and chairs to make the final decision, but also to judge the potential impact of the paper. In this paper, we propose to use aspect-based sentiment analysis of scientific reviews to be able to extract useful information, which correlates well with the accept/reject decision. While working on a dataset of close to 8k reviews from ICLR, one of the top conferences in the field of machine learning, we use an active learning framework to build a training dataset for aspect prediction, which is further used to obtain the aspects and sentiments for the entire dataset. We show that the distribution of aspect-based sentiments obtained from a review is significantly different for accepted and rejected papers. We use the aspect sentiments from these reviews to make an intriguing observation, certain aspects present in a paper and discussed in the review strongly determine the final recommendation. As a second objective, we quantify the extent of disagreement among the reviewers refereeing a paper. We also investigate the extent of disagreement between the reviewers and the chair and find that the inter-reviewer disagreement may have a link to the disagreement with the chair. One of the most interesting observations from this study is that reviews, where the reviewer score and the aspect sentiments extracted from the review text written by the reviewer are consistent, are also more likely to be concurrent with the chair's decision.Comment: Accepted in JCDL'2

ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing

Peer review is a cornerstone of science. Research communities conduct peer reviews to assess contributions and to improve the overall quality of science work. Every year, new community members are recruited as peer reviewers for the first time. How could technology help novices adhere to their community's practices and standards for peer reviewing? To better understand peer review practices and challenges, we conducted a formative study with 10 novices and 10 experts. We found that many experts adopt a workflow of annotating, note-taking, and synthesizing notes into well-justified reviews that align with community standards. Novices lack timely guidance on how to read and assess submissions and how to structure paper reviews. To support the peer review process, we developed ReviewFlow -- an AI-driven workflow that scaffolds novices with contextual reflections to critique and annotate submissions, in-situ knowledge support to assess novelty, and notes-to-outline synthesis to help align peer reviews with community expectations. In a within-subjects experiment, 16 inexperienced reviewers wrote reviews in two conditions: using ReviewFlow and using a baseline environment with minimal guidance. With ReviewFlow, participants produced more comprehensive reviews, identifying more pros and cons. While participants appreciated the streamlined process support from ReviewFlow, they also expressed concerns about using AI as part of the scientific review process. We discuss the implications of using AI to scaffold the peer review process on scientific work and beyond.Comment: 19 pages, accepted at the 29th ACM Conference on Intelligent User Interfaces (IUI 2024

Topic Modeling in Theory and Practice

Topic models can decompose a large corpus of text into a relatively small set of interpretable themes or topics, potentially enabling a domain expert to explore and analyze a corpus more efficiently. However, in my work, I have found that theories put forth by topic modeling research are not always borne out in practice. In this dissertation, I use case studies to explore four theories of topic modeling. While these theories are not explicitly stated, I show that they are communicated implicitly, some within an individual study and others more diffusely. I show that this implicit knowledge fails to hold in practice in the settings I consider. While my work is confined to topic modeling research and moreover concentrated on the latent Dirichlet allocation topic model, I argue that these kinds of gaps may pervade scientific research and present an obstacle to improving the diversity of the research community