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Smart Contract Generation Assisted by AI-Based Word Segmentation
Tong, Yu1; Tan, Weiming1; Guo, Jingzhi1; Shen, Bingqing2; Qin, Peng1; Zhuo, Shuaihe3
2022-05-09
Source PublicationApplied Sciences-Basel
ISSN2076-3417
Volume12Issue:9Pages:4773
Abstract

In the last decade, blockchain smart contracts emerged as an automated, decentralized, traceable, and immutable medium of value exchange. Nevertheless, existing blockchain smart contracts are not compatible with legal contracts. The automatic execution of a legal contract written in natural language is an open research question that can extend the blockchain ecosystem and inspire next-era business paradigms. In this paper, we propose an AI-assisted Smart Contract Generation (AIASCG) framework that allows contracting parties in heterogeneous contexts and different languages to collaboratively negotiate and draft the contract clauses. AIASCG provides a universal representation of contracts through the machine natural language (MNL) as the common understanding of the contract obligations. We compare the design of AIASCG with existing smart contract generation approaches to present its novelty. The main contribution of AIASCG is to address the issue in our previous proposed smart contract generation framework. For sentences written in natural language, existing framework requires editors to manually split sentences into words with semantic meaning. We propose an AI-based automatic word segmentation technique called Separation Inference (SpIn) to fulfill automatic split of the sentence. SpIn serves as the core component in AIASCG that accurately recommends the intermediate MNL outputs from a natural language sentence, tremendously reducing the manual effort in contract generation. SpIn is evaluated from a robustness and human satisfaction point of view to demonstrate its effectiveness. In the robustness evaluation, SpIn achieves state-of-the-art F1 scores and Recall of Out-of-Vocabulary (R_OOV) words on multiple word segmentation tasks. In addition, in the human evaluation, participants believe that 88.67% of sentences can be saved 80–100% of the time through automatic word segmentation.

KeywordSmart Contract Semantic Understanding Collaborative Drafting Automatic Word Segmentation
DOI10.3390/app12094773
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaChemistry ; Engineering ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000799231800001
Scopus ID2-s2.0-85130226881
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Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorZhuo, Shuaihe
Affiliation1.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, 999078, Macao
2.School of Software, Shanghai Jiao Tong University, Shanghai, 200240, China
3.School of Business, Macau University of Science and Technology, 999078, Macao
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Tong, Yu,Tan, Weiming,Guo, Jingzhi,et al. Smart Contract Generation Assisted by AI-Based Word Segmentation[J]. Applied Sciences-Basel, 2022, 12(9), 4773.
APA Tong, Yu., Tan, Weiming., Guo, Jingzhi., Shen, Bingqing., Qin, Peng., & Zhuo, Shuaihe (2022). Smart Contract Generation Assisted by AI-Based Word Segmentation. Applied Sciences-Basel, 12(9), 4773.
MLA Tong, Yu,et al."Smart Contract Generation Assisted by AI-Based Word Segmentation".Applied Sciences-Basel 12.9(2022):4773.
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