Domain Adaptation for an Automated Classification of Deontic Modalities in Software Engineering Contracts
Fri 27 Aug 2021 00:25 - 00:30 - Analytics & Software Evolution—Recommender Systems Chair(s): Juri Di Rocco
Contracts are agreements between parties engaging in economic transactions. They specify deontic modalities that the signatories should be held responsible for and state the penalties or actions to be taken if the stated agreements are not met. Additionally, contracts have also been known to be source of Software Engineering (SE) requirements. Identifying the deontic modalities in contracts can therefore add value to the Requirements Engineering (RE) phase of SE. The complex and ambiguous language of contracts make it difficult and time-consuming to identify the deontic modalities (obligations, permissions, prohibitions), embedded in the text. State-of-art neural network models are effective for text classification; however, they require substantial amounts of training data. The availability of contracts data is sparse owing to the confidentiality concerns of customers. In this paper, we leverage the linguistic and taxonomical similarities between regulations (available abundantly in the public domain) and contracts to demonstrate that it is possible to use regulations as training data for classifying deontic modalities in real-life contracts. We discuss the results of a range of experiments from the use of rule-based approach to Bidirectional Encoder Representations from Transformers (BERT) for automating the classification of deontic modalities. With BERT, we obtained an average precision and recall of 90% and 89.66% respectively.
Thu 26 AugDisplayed time zone: Athens change
12:00 - 13:00 | Analytics & Software Evolution—Recommender SystemsDemonstrations / Industry Papers / Research Papers +12h Chair(s): Phuong T. Nguyen University of L’Aquila, Gabriele Bavota Università della Svizzera italiana (USI) | ||
12:00 10mPaper | Which Abbreviations Should Be Expanded? Research Papers Yanjie Jiang Beijing Institute of Technology, Hui Liu Beijing Institute of Technology, Yuxia Zhang Beijing Institute of Technology, Nan Niu University of Cincinnati, Yuhai Zhao Northeastern University, Lu Zhang Peking University DOI | ||
12:10 5mPaper | BRAID: An API Recommender Supporting Implicit User Feedback Demonstrations Yu Zhou Nanjing University of Aeronautics and Astronautics, Haonan Jin Nanjing University of Aeronautics and Astronautics, Xinying Yang Nanjing University of Aeronautics and Astronautics, Taolue Chen University of London, Krishna Narasimhan TU Darmstadt, Harald Gall University of Zurich DOI | ||
12:15 5mPaper | Code2Que: A Tool for Improving Question Titles from Mined Code Snippets in Stack Overflow Demonstrations Zhipeng Gao Monash University, Xin Xia Huawei Technologies, David Lo Singapore Management University, John Grundy Monash University, Yuan-Fang Li Monash University DOI | ||
12:20 5mPaper | StackEmo: Towards Enhancing User Experience by Augmenting Stack Overflow with Emojis Demonstrations DOI Media Attached | ||
12:25 5mPaper | Domain Adaptation for an Automated Classification of Deontic Modalities in Software Engineering Contracts Industry Papers DOI | ||
12:30 30mLive Q&A | Q&A (Analytics & Software Evolution—Recommender Systems) Research Papers |
Fri 27 AugDisplayed time zone: Athens change
00:00 - 01:00 | Analytics & Software Evolution—Recommender SystemsDemonstrations / Research Papers / Industry Papers Chair(s): Juri Di Rocco University of L'Aquila | ||
00:00 10mPaper | Which Abbreviations Should Be Expanded? Research Papers Yanjie Jiang Beijing Institute of Technology, Hui Liu Beijing Institute of Technology, Yuxia Zhang Beijing Institute of Technology, Nan Niu University of Cincinnati, Yuhai Zhao Northeastern University, Lu Zhang Peking University DOI | ||
00:10 5mPaper | BRAID: An API Recommender Supporting Implicit User Feedback Demonstrations Yu Zhou Nanjing University of Aeronautics and Astronautics, Haonan Jin Nanjing University of Aeronautics and Astronautics, Xinying Yang Nanjing University of Aeronautics and Astronautics, Taolue Chen University of London, Krishna Narasimhan TU Darmstadt, Harald Gall University of Zurich DOI | ||
00:15 5mPaper | Code2Que: A Tool for Improving Question Titles from Mined Code Snippets in Stack Overflow Demonstrations Zhipeng Gao Monash University, Xin Xia Huawei Technologies, David Lo Singapore Management University, John Grundy Monash University, Yuan-Fang Li Monash University DOI | ||
00:20 5mPaper | StackEmo: Towards Enhancing User Experience by Augmenting Stack Overflow with Emojis Demonstrations DOI Media Attached | ||
00:25 5mPaper | Domain Adaptation for an Automated Classification of Deontic Modalities in Software Engineering Contracts Industry Papers DOI | ||
00:30 30mLive Q&A | Q&A (Analytics & Software Evolution—Recommender Systems) Research Papers |