JEC-QA: A Legal-Domain Question Answering Dataset

We present JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China. The examination is a comprehensive evaluation of professional skills for legal practitioners. College students are required to pass the examination to be certified as a lawyer or a judge. The dataset is challenging for existing question answering methods, because both retrieving relevant materials and answering questions require the ability of logic reasoning. Due to the high demand of multiple reasoning abilities to answer legal questions, the state-of-the-art models can only achieve about 28% accuracy on JEC-QA, while skilled humans and unskilled humans can reach 81% and 64% accuracy respectively, which indicates a huge gap between humans and machines on this task. We will release JEC-QA and our baselines to help improve the reasoning ability of machine comprehension models. You can access the dataset from http://jecqa.thunlp.org/.Comment: 9 pages, 2 figures, 10 tables, accepted by AAAI202

Adversarial Language Games for Advanced Natural Language Intelligence

We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work, we propose a challenging adversarial language game called Adversarial Taboo as an example, in which an attacker and a defender compete around a target word. The attacker is tasked with inducing the defender to utter the target word invisible to the defender, while the defender is tasked with detecting the target word before being induced by the attacker. In Adversarial Taboo, a successful attacker must hide its intention and subtly induce the defender, while a competitive defender must be cautious with its utterances and infer the intention of the attacker. Such language abilities can facilitate many important downstream NLP tasks. To instantiate the game, we create a game environment and a competition platform. Comprehensive experiments and empirical studies on several baseline attack and defense strategies show promising and interesting results. Based on the analysis on the game and experiments, we discuss multiple promising directions for future research.Comment: Accepted by AAAI 202

Determination of hydroxyl groups in biorefinery resources via quantitative 31P NMR spectroscopy

The analysis of chemical structural characteristics of biorefinery product streams (such as lignin and tannin) has advanced substantially over the past decade, with traditional wet-chemical techniques being replaced or supplemented by NMR methodologies. Quantitative 31P NMR spectroscopy is a promising technique for the analysis of hydroxyl groups because of its unique characterization capability and broad potential applicability across the biorefinery research community. This protocol describes procedures for (i) the preparation/solubilization of lignin and tannin, (ii) the phosphitylation of their hydroxyl groups, (iii) NMR acquisition details, and (iv) the ensuing data analyses and means to precisely calculate the content of the different types of hydroxyl groups. Compared with traditional wet-chemical techniques, the technique of quantitative 31P NMR spectroscopy offers unique advantages in measuring hydroxyl groups in a single spectrum with high signal resolution. The method provides complete quantitative information about the hydroxyl groups with small amounts of sample (~30 mg) within a relatively short experimental time (~30-120 min)

A Novel Design of an Inner Rotor for Optimizing the Air-Gap Magnetic Field of Hollow-Cup Motors

In order to obtain a high power density, spacecraft usually use hollow-cup motors with trapezoidal air-gap magnetic field waveforms. However, due to structural issues, the hollow-cup motor has the problem that the waveform of the air-gap magnetic field is inconsistent with the ideal trapezoidal waveform, which causes torque ripples. In order to reduce torque ripples, the existing method only changes the structure of PMs; the changed PMs are difficult to magnetize and manufacture, which causes the air-gap magnetic field waveform to be unsuitable as the ideal waveform. This paper proposes a novel design of an inner rotor of a hollow-cup motor with an eccentric inner rotor based on the characteristics that the hollow-cup motor has inner and outer rotors and the two rotors rotate synchronously during operation. First, the influencing factors of the air-gap magnetic field are analyzed and the mathematical model of the eccentric inner rotor is established. Then, an eccentric model is established by finite element analysis, which proves that the eccentricity of the inner rotor can make the air-gap magnetic field waveform closer to the ideal trapezoid. Finally, a prototype based on the optimal eccentricity value is developed, verifying the effectiveness of the novel design of the inner rotor

Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction

Legal Judgment Prediction (LJP) aims to predict judgment results according to the facts of cases. In recent years, LJP has drawn increasing attention rapidly from both academia and the legal industry, as it can provide references for legal practitioners and is expected to promote judicial justice. However, the research to date usually suffers from the lack of interpretability, which may lead to ethical issues like inconsistent judgments or gender bias. In this paper, we present QAjudge, a model based on reinforcement learning to visualize the prediction process and give interpretable judgments. QAjudge follows two essential principles in legal systems across the world: Presumption of Innocence and Elemental Trial. During inference, a Question Net will select questions from the given set and an Answer Net will answer the question according to the fact description. Finally, a Predict Net will produce judgment results based on the answers. Reward functions are designed to minimize the number of questions asked. We conduct extensive experiments on several real-world datasets. Experimental results show that QAjudge can provide interpretable judgments while maintaining comparable performance with other state-of-the-art LJP models. The codes can be found from https://github.com/thunlp/QAjudge