De-Biased Court's View Generation with Causality

Court's view generation is a novel but essential task for legal AI, aiming at improving the interpretability of judgment prediction results and enabling automatic legal document generation. While prior text-to-text natural language generation (NLG) approaches can be used to address this problem, neglecting the confounding bias from the data generation mechanism can limit the model performance, and the bias may pollute the learning outcomes. In this paper, we propose a novel Attentional and Counterfactual based Natural Language Generation (AC-NLG) method, consisting of an attentional encoder and a pair of innovative counterfactual decoders. The attentional encoder leverages the plaintiff's claim and fact description as input to learn a claim-aware encoder from which the claim-related information in fact description can be emphasized. The counterfactual decoders are employed to eliminate the confounding bias in data and generate judgmentdiscriminative court's views (both supportive and non-supportive views) by incorporating with a synergistic judgment predictive model. Comprehensive experiments show the effectiveness of our method under both quantitative and qualitative evaluation metrics. PLAINTIFF'S CLAIM The plaintiff A claimed that the defendant B should return the loan of $29,500 Principle Claim and the corresponding interest Interest Claim . FACT DESCRIPTION After the hearing, the court held the facts as follows: The defendant B borrowed$29,500 from the plaintiff A, and agreed to return after one month. After the loan expired, the defendant failed to return Fact . COURT'S VIEW The court concluded that the loan relationship between the plaintiff A and the defendant B is valid. The defendant failed to return the money on time Ra tiona le . Therefore, the plaintiff's claim on principle was supported Acceptance according to law. The court did not support the plaintiff's claim on interest Rejection because the evidence was insufficient Rationale .