学习检查合同信息
Learning to Check Contract Inconsistencies
摘要：
合同的一致性对于保证合同的法律效力是很重要的。在许多情况下，合同是通过在预编译的表单中填充空白来编写的。由于粗心，两个应该填写相同（或不同）内容的空白可能会被错误地填写不同（或相同）内容。这将导致合同不一致的问题，这可能会严重损害合同的法律效力。解决这一问题的传统方法主要依赖于人工合同审查，这是劳动密集型和昂贵的。在这项工作中，我们制定了一个新的合同不一致性检查（CIC）问题，并设计了一个端到端的框架，称为成对空白决议（PBR），以解决高精度的CIC问题。我们的PBR模型包含一个新的BlankCoder，以解决建模无意义的空白的挑战。BlankCoder采用两阶段注意力机制，充分关联无意义的空白与其相关的描述，同时避免纳入无关的上下文单词。在真实数据集上进行的实验表明，该方法在CIC问题中的平衡准确率为94.05%，F1得分为90.90%。
英文摘要：
Contract consistency is important in ensuring the legal validity of the contract. In many scenarios, a contract is written by filling the blanks in a precompiled form. Due to carelessness, two blanks that should be filled with the same (or different)content may be incorrectly filled with different (or same) content. This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract. Traditional methods to address this issue mainly rely on manual contract review, which is labor-intensive and costly. In this work, we formulate a novel Contract Inconsistency Checking (CIC) problem, and design an end-to-end framework, called Pair-wise Blank Resolution (PBR), to solve the CIC problem with high accuracy. Our PBR model contains a novel BlankCoder to address the challenge of modeling meaningless blanks. BlankCoder adopts a two-stage attention mechanism that adequately associates a meaningless blank with its relevant descriptions while avoiding the incorporation of irrelevant context words. Experiments conducted on real-world datasets show the promising performance of our method with a balanced accuracy of 94.05% and an F1 score of 90.90% in the CIC problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
