Year: 2020
Type: conference-paper
Abstract: Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.However, most existing methods either use handcrafted features or learn features using deep learning with simple text corpus as input for review rating prediction, ignoring the hierarchies among data.In this paper, we propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation, which can serve as an effective decision-making tool for the academic paper review process.Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and interreview encoder (level three).Each encoder first derives contextual representation of each level, then generates a higher-level representation, and after the learning process, we are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.Furthermore, we introduce two new metrics to evaluate models in data imbalance situations.Extensive experiments on a publicly available dataset (PeerRead) and our own collected dataset (OpenReview) demonstrate the superiority of the proposed approach compared with state-of-the-art methods.
Authors: Zhongfen Deng, Hao Peng, Congying Xia, Jianxin Li, Lifang He, Philip Yu
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations, III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation, 大规模网络中异常子图分析理论与算法研究, G4815838450, G486159857, G544703200, G6898366254, SaTC: CORE: Small: Collaborative: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers
OpenAlex ID: W3110228087