Legal judgment prediction (LJP) is used to predict judgment results based on the description of individual legal cases. In order to be more suitable for actual application scenarios in which the case has cited multiple articles and has multiple charges, we formulate legal judgment prediction as a multiple label learning problem and present a deep learning model that can effectively encode the content of each legal case via a multi-residual convolution neural network and the semantics of law articles via an article encoder. An article-wise attention mechanism is proposed to fuse the two types of encoded information. Experimental results derived on the CAIL2018 datasets show that our model provides a significant performance improvement over the existing neural models in predicting relevant law articles and charges.

Legal Judgment Prediction aims to automatically predict judgment outcomes based on descriptions of legal cases and established law articles, and has received increasing attention. In the preliminary work, several problems still have not been adequately solved. One is how to utilize limited but valuable label information. Existing methods mostly ignore the gap between the description of established articles and cases, but directly integrate them. Second, most studies ignore the mutual constraint among the subtasks, such as logically or semantically, each charge is only related to some specific articles. To address these issues, we first construct a crime similarity graph and then perform a distillation operation to collect discriminate keywords for each charge. Furthermore, we fuse these discriminative keywords instead of established article descriptions into case embedding with a cross-attention mechanism to obtain deep semantic representations of cases incorporating label information. Finally, under a constraint among subtasks, we optimize the one-hot representation of ground-truth labels to guarantee consistent results across the subtasks based on the label-enhancement algorithm. To verify the effectiveness and robustness of our framework, we conduct extensive experiments on two public datasets. The experimental results show that the proposed method outperforms the state-of-art models by 3.89%/7.92% and 1.23%/2.50% in the average MF1-score of the subtasks on CAIL-Small/Big, respectively.

As one of the most critical tasks in legal artificial intelligence, legal judgment prediction (LJP) has garnered growing attention, especially in the civil law system. However, current methods often overlook the challenge of imbalanced label distributions, treating each label with equal importance, which can lead the model to be biased toward labels with high frequency. In this paper, we propose a label-enhanced prototypical network (LPN) suitable for LJP, that adopts a strategy of uniform encoding and separate decoding. Specifically, LPN adopts a multi-scale convolutional neural network to uniformly encode case factual description to capture long-distance features of the document. At the decoding end, a prototypical network incorporating label semantic features is used to guide the learning of prototype representations of high-frequency and low-frequency labels, respectively. At the same time, we also propose a prototype-prototype loss to optimize the prototypical representation. We conduct extensive experiments on two real datasets and show that our proposed method effectively improves the performance of LJP, with an average F1 of 1.23% and 1.13% higher than the state-of-the-art model on two subtasks, respectively.

Emotion Recognition in Conversation (ERC) aims to recognize the emotion for each utterance in a conversation automatically. Due to the difficulty of collecting and labeling, this task lacks the dataset corpora available on a large scale. This increases the difficulty of finishing the supervised training required by large-scale neural networks. Introducing the large-scale generative conversational dataset can assist with modeling dialogue. However, the spatial distribution of feature vectors in the source and target domains is inconsistent after introducing the external dataset. To alleviate the problem, we propose a Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation (DAN-CDERC) model, consisting of domain adversarial and emotion recognition models. The domain adversarial model consists of the encoders, a generator and a domain discriminator. First, the encoders and generator learn contextual features from a large-scale source dataset. The discriminator performs domain adaptation by discriminating the domain to make the feature space of the source and target domain consistent, so as to obtain domain invariant features. Then DAN-CDERC transfers the learned domain invariant dialogue context knowledge from the domain adversarial model to the emotion recognition model to assist in modeling the dialogue context. Due to the use of a domain adversarial network, DAN-CDERC obtains dialogue-level contextual information that is domain invariant, thereby reducing the negative impact of inconsistency in domain space. Empirical studies illustrate that the proposed model outperforms the baseline models on three benchmark emotion recognition datasets.