ScienceQA: a novel resource for question answering on scholarly articles.

IF 1.6 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE International Journal on Digital Libraries Pub Date : 2022-01-01 Epub Date: 2022-07-20 DOI:10.1007/s00799-022-00329-y
Tanik Saikh, Tirthankar Ghosal, Amish Mittal, Asif Ekbal, Pushpak Bhattacharyya
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引用次数: 10

Abstract

Machine Reading Comprehension (MRC) of a document is a challenging problem that requires discourse-level understanding. Information extraction from scholarly articles nowadays is a critical use case for researchers to understand the underlying research quickly and move forward, especially in this age of infodemic. MRC on research articles can also provide helpful information to the reviewers and editors. However, the main bottleneck in building such models is the availability of human-annotated data. In this paper, firstly, we introduce a dataset to facilitate question answering (QA) on scientific articles. We prepare the dataset in a semi-automated fashion having more than 100k human-annotated context-question-answer triples. Secondly, we implement one baseline QA model based on Bidirectional Encoder Representations from Transformers (BERT). Additionally, we implement two models: the first one is based on Science BERT (SciBERT), and the second is the combination of SciBERT and Bi-Directional Attention Flow (Bi-DAF). The best model (i.e., SciBERT) obtains an F1 score of 75.46%. Our dataset is novel, and our work opens up a new avenue for scholarly document processing research by providing a benchmark QA dataset and standard baseline. We make our dataset and codes available here at https://github.com/TanikSaikh/Scientific-Question-Answering.

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ScienceQA:一个新颖的学术文章问答资源。
文档的机器阅读理解(MRC)是一个具有挑战性的问题,需要语篇级的理解。从学术文章中提取信息是研究人员快速理解基础研究并向前发展的关键用例,特别是在这个信息大流行的时代。研究文章的MRC也可以为审稿人和编辑提供有用的信息。然而,构建此类模型的主要瓶颈是人工注释数据的可用性。在本文中,我们首先引入一个数据集来促进科学文章的问答(QA)。我们以半自动的方式准备数据集,拥有超过10万个人工注释的上下文-问题-答案三元组。其次,我们实现了一个基于变形金刚双向编码器表示(BERT)的基线QA模型。此外,我们还实现了两个模型:第一个是基于科学BERT (SciBERT)的模型,第二个是SciBERT和双向注意流(Bi-DAF)的结合模型。最佳模型SciBERT的F1得分为75.46%。我们的数据集是新颖的,我们的工作通过提供基准QA数据集和标准基线,为学术文档处理研究开辟了一条新的途径。我们在https://github.com/TanikSaikh/Scientific-Question-Answering上提供了我们的数据集和代码。
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来源期刊
CiteScore
4.30
自引率
6.70%
发文量
20
期刊介绍: The International Journal on Digital Libraries (IJDL) examines the theory and practice of acquisition definition organization management preservation and dissemination of digital information via global networking. It covers all aspects of digital libraries (DLs) from large-scale heterogeneous data and information management & access to linking and connectivity to security privacy and policies to its application use and evaluation.The scope of IJDL includes but is not limited to: The FAIR principle and the digital libraries infrastructure Findable: Information access and retrieval; semantic search; data and information exploration; information navigation; smart indexing and searching; resource discovery Accessible: visualization and digital collections; user interfaces; interfaces for handicapped users; HCI and UX in DLs; Security and privacy in DLs; multimodal access Interoperable: metadata (definition management curation integration); syntactic and semantic interoperability; linked data Reusable: reproducibility; Open Science; sustainability profitability repeatability of research results; confidentiality and privacy issues in DLs Digital Library Architectures including heterogeneous and dynamic data management; data and repositories Acquisition of digital information: authoring environments for digital objects; digitization of traditional content Digital Archiving and Preservation Digital Preservation and curation Digital archiving Web Archiving Archiving and preservation Strategies AI for Digital Libraries Machine Learning for DLs Data Mining in DLs NLP for DLs Applications of Digital Libraries Digital Humanities Open Data and their reuse Scholarly DLs (incl. bibliometrics altmetrics) Epigraphy and Paleography Digital Museums Future trends in Digital Libraries Definition of DLs in a ubiquitous digital library world Datafication of digital collections Interaction and user experience (UX) in DLs Information visualization Collection understanding Privacy and security Multimodal user interfaces Accessibility (or "Access for users with disabilities") UX studies
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