Residential College | false |
Status | 已發表Published |
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 Publication | Applied Sciences-Basel |
ISSN | 2076-3417 |
Volume | 12Issue: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. |
Keyword | Smart Contract Semantic Understanding Collaborative Drafting Automatic Word Segmentation |
DOI | 10.3390/app12094773 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Chemistry ; Engineering ; Materials Science ; Physics |
WOS Subject | Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied |
WOS ID | WOS:000799231800001 |
Scopus ID | 2-s2.0-85130226881 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Science and Technology DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Corresponding Author | Zhuo, Shuaihe |
Affiliation | 1.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 Affilication | Faculty of Science and Technology |
Corresponding Author Affilication | University 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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