Argument Mining for Understanding Peer Reviews (1903.10104v1)

Abstract: Peer-review plays a critical role in the scientific writing and publication ecosystem. To assess the efficiency and efficacy of the reviewing process, one essential element is to understand and evaluate the reviews themselves. In this work, we study the content and structure of peer reviews under the argument mining framework, through automatically detecting (1) argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). We first collect 14.2K reviews from major machine learning and natural language processing venues. 400 reviews are annotated with 10,386 propositions and corresponding types of Evaluation, Request, Fact, Reference, or Quote. We then train state-of-the-art proposition segmentation and classification models on the data to evaluate their utilities and identify new challenges for this new domain, motivating future directions for argument mining. Further experiments show that proposition usage varies across venues in amount, type, and topic.

Argument Mining for Understanding Peer Reviews: A Technical Summary

Peer review is an integral mechanism in the academic publishing landscape, guiding the validation and dissemination of scientific findings. The paper "Argument Mining for Understanding Peer Reviews" by Xinyu Hua et al. introduces an argument mining approach aimed at elucidating the content and structure of peer reviews. The authors have developed a framework centered around the automatic detection of argumentative propositions and their categorization, which presents novel insights into the dynamics of peer review processes.

Methodology

The authors collected and annotated a substantial corpus comprising 14,200 peer reviews from major ML and NLP conferences. Within these, 400 reviews were meticulously annotated, identifying propositions across categories such as Evaluation, Request, Fact, Reference, and Quote. The annotated dataset, referred to as AMPERE, serves as a foundation for training models designed to segment and classify argumentative propositions.

The segmentation and classification processes employ state-of-the-art approaches, including Conditional Random Fields (CRF) and BiLSTM-CRF networks, augmented with ELMo contextual word embeddings. The segmentation task uncovered propositions within sub-sentence boundaries, presenting challenges distinct from those in traditional textual domains like essays or social media. The classification task further categorized the propositions into the predefined types, demonstrating variability in linguistic expression across reviewing venues.

Results

The paper reports significant findings from experiments conducted using AMPERE. The BiLSTM-CRF model achieved a superior performance with an F1 score of 81.09% for proposition segmentation, highlighting its efficacy in navigating the complex linguistic structures inherent in peer reviews. Classification results revealed that the CNN model outperformed others when gold-standard segments were provided, illustrating the impact of data size on neural model performance.

The analysis extends to unlabeled reviews from conferences such as ICLR, UAI, ACL, and NeurIPS, where the proposition usage varied significantly. ACL reviews generated more requests but fewer factual propositions compared to ML venues, an insight which may reflect domain-specific reviewer expectations and norms.

Implications

The insights from argument mining have substantial implications. Practically, the enhanced understanding of review structures can inform the development of automated systems for review quality assessment, potentially augmenting editorial decision-making processes. Theoretically, the exploration of argumentative dynamics enriches the understanding of discourse analysis, with broader applications in natural language processing tasks.

Looking forward, the challenges identified in segmenting and classifying propositions indicate directions for advancing argument mining methodologies. The research suggests a need for developing models that better capture the intricacies of argumentative structures specific to academic reviews, possibly incorporating more sophisticated discourse analysis and transfer learning techniques to adapt across diverse genres.

In conclusion, the work by Hua et al. underscores the potential of argument mining in transforming how peer reviews are analyzed and understood, representing a critical stepping stone towards more refined, automated systems in scholarly communication.