一句话：将公开法规作为训练数据，通过领域自适应自动分类合同中的道义模态。
本文研究软件工程合同中道义模态（义务、许可、禁止）的自动分类。合同是软件需求的重要来源，识别这些模态有助于需求工程，但合同语言复杂、含糊，且因客户保密而缺乏训练数据。作者利用公开法规与合同在语言和分类上的相似性，将法规作为训练数据实现领域自适应，并开展从规则方法到 BERT 的一系列实验。在真实合同上的评估表明，BERT 的平均精度和召回率分别达到 90% 和 89.66%，说明用法规数据可缓解合同标注稀缺问题，支持道义模态自动识别。
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.
DOI 原文 ·
@inproceedings{paperbot6346,
title = {Domain adaptation for an automated classification of deontic modalities in software engineering contracts},
author = {Vivek Joshi and Preethu Rose Anish and Smita Ghaisas},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473921}
}