SECaps: A Sequence Enhanced Capsule Model for Charge Prediction

Automatic charge prediction aims to predict appropriate final charges according to the fact descriptions for a given criminal case. Automatic charge prediction plays a critical role in assisting judges and lawyers to improve the efficiency of legal decisions, and thus has received much attention. Nevertheless, most existing works on automatic charge prediction perform adequately on high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. In this paper, we propose a Sequence Enhanced Capsule model, dubbed as SECaps model, to relieve this problem. Specifically, following the work of capsule networks, we propose the seq-caps layer, which considers sequence information and spatial information of legal texts simultaneously. Then we design a attention residual unit, which provides auxiliary information for charge prediction. In addition, our SECaps model introduces focal loss, which relieves the problem of imbalanced charges. Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4% absolutely considerable improvements under Macro F1 in Criminal-S and Criminal-L respectively. The experimental results consistently demonstrate the superiorities and competitiveness of our proposed model.Comment: 13 pages, 3figures, 5 table

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

When Intelligence is (Dys)Functional for Achieving Sales Performance

Using two different samples of salespeople, the authors investigate how a combination of general mental ability (GMA) and specific skills and capabilities (social competence and thinking styles) allows salespeople to reach their sales goals. The study finds evidence for an interaction between GMA and social competence. If combined with high social competence, high GMA leads to highest sales performance; if combined with low social competence, high GMA leads to lowest sales performance. In addition, interaction effects between GMA and a judicial thinking style were found. Salespeople high on GMA have the most potential for attaining high levels of sales performance when combined with specific skills; when lacking these skills they may become the firm’s worst performers.sales;knowledge;general mental ability;knowledge based marketing;thinking styles

Brazilian Court Documents Clustered by Similarity Together Using Natural Language Processing Approaches with Transformers

Recent advances in Artificial intelligence (AI) have leveraged promising results in solving complex problems in the area of Natural Language Processing (NLP), being an important tool to help in the expeditious resolution of judicial proceedings in the legal area. In this context, this work targets the problem of detecting the degree of similarity between judicial documents that can be achieved in the inference group, by applying six NLP techniques based on transformers, namely BERT, GPT-2 and RoBERTa pre-trained in the Brazilian Portuguese language and the same specialized using 210,000 legal proceedings. Documents were pre-processed and had their content transformed into a vector representation using these NLP techniques. Unsupervised learning was used to cluster the lawsuits, calculating the quality of the model based on the cosine of the distance between the elements of the group to its centroid. We noticed that models based on transformers present better performance when compared to previous research, highlighting the RoBERTa model specialized in the Brazilian Portuguese language, making it possible to advance in the current state of the art in the area of NLP applied to the legal sector