Discussion on Chinese Public Security Organs’ Anti-Corruption Campaign

In a market economy, there are many problems in public security organs, such as the commercialization of police powers, serious attitudes of entitlement among police officers, strong acquisitiveness and so on. The main root causes include a missing of principle of having a policy for the public interest, missing core values of the People’s Police, and lack of oversight mechanisms on police powers. To solve these problems, we should strengthen education, establish a good concept of law enforcement, curb corruption desire, improve the supervision mechanism, build a corruption defense line, preferentially treat police, and enhance police officers’ awareness of anti-corruption so as to improve the anti-corruption campaign in public security organs

Discussion on Police Culture Construction

Police culture is ideological support for promoting police construction, which is a spiritual weapon that increases strength and sense of mission of policemen. It is necessary to vigorously carry forward the public security culture, carry out the construction of police culture, and actively explore the effective construction path of China’s police culture, focusing on policeman core value, being promoted by police culture innovation, and aiming at successfully processing police works

On the Exploration of the Construction of Consecutive Anti-Corruption Mechanism in Public Security Organ in the New Era

Based on the guidance of socialism with specific Chinese characteristics put forth by President Xi Jinping in this new era, this paper exposes the existing corruption problem and analyses the reasons, comes up with the Construction of Consecutive Anti-corruption Mechanism in Public Security Organ in the new era. This is not only conducive to the adherence of general principle of “Be royal to the CPC, service for the people, enforce the justice of law and obeying the strict discipline”, but also of great significance for the anti-corruption campaign in the public security organ

Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates

Calibration strengthens the trustworthiness of black-box models by producing better accurate confidence estimates on given examples. However, little is known about if model explanations can help confidence calibration. Intuitively, humans look at important features attributions and decide whether the model is trustworthy. Similarly, the explanations can tell us when the model may or may not know. Inspired by this, we propose a method named CME that leverages model explanations to make the model less confident with non-inductive attributions. The idea is that when the model is not highly confident, it is difficult to identify strong indications of any class, and the tokens accordingly do not have high attribution scores for any class and vice versa. We conduct extensive experiments on six datasets with two popular pre-trained language models in the in-domain and out-of-domain settings. The results show that CME improves calibration performance in all settings. The expected calibration errors are further reduced when combined with temperature scaling. Our findings highlight that model explanations can help calibrate posterior estimates.Comment: EMNLP 202

SeDR: Segment Representation Learning for Long Documents Dense Retrieval

Recently, Dense Retrieval (DR) has become a promising solution to document retrieval, where document representations are used to perform effective and efficient semantic search. However, DR remains challenging on long documents, due to the quadratic complexity of its Transformer-based encoder and the finite capacity of a low-dimension embedding. Current DR models use suboptimal strategies such as truncating or splitting-and-pooling to long documents leading to poor utilization of whole document information. In this work, to tackle this problem, we propose Segment representation learning for long documents Dense Retrieval (SeDR). In SeDR, Segment-Interaction Transformer is proposed to encode long documents into document-aware and segment-sensitive representations, while it holds the complexity of splitting-and-pooling and outperforms other segment-interaction patterns on DR. Since GPU memory requirements for long document encoding causes insufficient negatives for DR training, Late-Cache Negative is further proposed to provide additional cache negatives for optimizing representation learning. Experiments on MS MARCO and TREC-DL datasets show that SeDR achieves superior performance among DR models, and confirm the effectiveness of SeDR on long document retrieval

Prompt-based Text Entailment for Low-Resource Named Entity Recognition

Pre-trained Language Models (PLMs) have been applied in NLP tasks and achieve promising results. Nevertheless, the fine-tuning procedure needs labeled data of the target domain, making it difficult to learn in low-resource and non-trivial labeled scenarios. To address these challenges, we propose Prompt-based Text Entailment (PTE) for low-resource named entity recognition, which better leverages knowledge in the PLMs. We first reformulate named entity recognition as the text entailment task. The original sentence with entity type-specific prompts is fed into PLMs to get entailment scores for each candidate. The entity type with the top score is then selected as final label. Then, we inject tagging labels into prompts and treat words as basic units instead of n-gram spans to reduce time complexity in generating candidates by n-grams enumeration. Experimental results demonstrate that the proposed method PTE achieves competitive performance on the CoNLL03 dataset, and better than fine-tuned counterparts on the MIT Movie and Few-NERD dataset in low-resource settings.Comment: COLING 202

The Application of Situational Teaching in Public Security Management Courses

As the educational mode in public security management specialty has transformed from the traditional academic type to the application type, the teaching methods are required to be reformed accordingly. Courses of the public security management specialty are featured by practicability, pertinence, operability and the clarity of knowledge points. Therefore, situational teaching method is suitable for teaching in public security management courses. The situational teaching mode in the public security management specialty connects professional construction characteristics with core curriculum. It is also based on the nature and features of the courses. Teachers can choose whole media simulation module, case analysis module, problem discussion module, role playing module, actual practice module and other situational teaching modes with diversity and wide angles. As a result, teachers can inspire learning enthusiasm of students and cultivate generalist of practical talent in public security management. Furthermore, the public security forces are largely improved