Learning to Predict Charges for Criminal Cases with Legal Basis

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.Comment: 10 pages, accepted by EMNLP 201

Improving first order temporal fact extraction with unreliable data

In this paper, we deal with the task of extracting first order temporal facts from free text. This task is a subtask of relation extraction and it aims at extracting relations between entity and time. Currently, the field of relation extraction mainly focuses on extracting relations between entities. However, we observe that the multi-granular nature of time expressions can help us divide the dataset constructed by distant supervision to reliable and less reliable subsets, which can help to improve the extraction results on relations between entity and time. We accordingly contribute the first dataset focusing on the first order temporal fact extraction task using distant supervision. To fully utilize both the reliable and the less reliable data, we propose to use curriculum learning to rearrange the training procedure, label dropout to make the model be more conservative about less reliable data, and instance attention to help the model distinguish important instances from unimportant ones. Experiments show that these methods help the model outperform the model trained purely on the reliable dataset as well as the model trained on the dataset where all subsets are mixed together

Experimental and physical model of the melting zone in the interface of the explosive cladding bar

AbstractLocal melting zone encountered in sections of the cladding interface is a distinguished phenomenon of the explosive cladding technique. The thickness and morphology of the melting zone in the Ti/NiCr explosive cladding bar are investigated by means of optical microscopy. Results show that the distribution of the melting zone in the interface of the Ti/NiCr explosive cladding bar is uniform and axisymmetric, and boundaries of the melting zone are circular arcs, whose center points to the center of the NiCr bar. The bamboo-shaped cracks generate in the melting zone. The thickness of the melting zone decreases with reducing of the stand-off distance and the thickness of the explosive. A physical model of the melting zone in the interface of the explosive cladding bar is proposed

Integrating Relation Constraints with Neural Relation Extractors

Recent years have seen rapid progress in identifying predefined relationship between entity pairs using neural networks NNs. However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints. These constraints are often defined over combinations of entity-relation-entity triples, since there often lack of explicitly well-defined type and cardinality requirements for the relations. In this paper, we propose a unified framework to integrate relation constraints with NNs by introducing a new loss term, ConstraintLoss. Particularly, we develop two efficient methods to capture how well the local predictions from multiple instance pairs satisfy the relation constraints. Experiments on both English and Chinese datasets show that our approach can help NNs learn from discrete relation constraints to reduce inconsistency among local predictions, and outperform popular neural relation extraction NRE models even enhanced with extra post-processing. Our source code and datasets will be released at https://github.com/PKUYeYuan/Constraint-Loss-AAAI-2020.Comment: Accepted to AAAI-202

Playing 20 Question Game with Policy-Based Reinforcement Learning

The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity. In the game, the answerer first thinks of an object such as a famous person or a kind of animal. Then the questioner tries to guess the object by asking 20 questions. In a Q20 game system, the user is considered as the answerer while the system itself acts as the questioner which requires a good strategy of question selection to figure out the correct object and win the game. However, the optimal policy of question selection is hard to be derived due to the complexity and volatility of the game environment. In this paper, we propose a novel policy-based Reinforcement Learning (RL) method, which enables the questioner agent to learn the optimal policy of question selection through continuous interactions with users. To facilitate training, we also propose to use a reward network to estimate the more informative reward. Compared to previous methods, our RL method is robust to noisy answers and does not rely on the Knowledge Base of objects. Experimental results show that our RL method clearly outperforms an entropy-based engineering system and has competitive performance in a noisy-free simulation environment.Comment: Accepted by EMNLP 201

Topological linear magnetoresistivity and thermoconductivity induced by noncentrosymmetric Berry curvature

The Berry curvature plays a key role in the magnetic transport of topological materials. Yet, it is not clear whether the Berry curvature by itself can give rise to universal transport phenomena with specific scaling behaviors. In this work, based on the semiclassical Boltzmann formalism and the symmetry analysis, we show that the noncentrosymmetric distribution of the Berry curvature generally results in linear magnetoresistivity and thermoconductivity both exhibiting the B-scaling behavior. We then study such kind of topological linear magnetoresistivity in the 2D MnBi2Te4 flakes and the 3D spin-orbit-coupled electron gas, the former showing good agreement with the experimental observations. The difference between our mechanism and the conventional anisotropic magnetoresistance is elucidated. Our theory proposes a universal scenario for the topological linear magnetoresistivity and thermoconductivity and predicts such effects to occur in various materials, which also provides a reasonable explanation for the recent observations of linear magnetoresistivity

Multi-choice Question Answering System of WIP at NTCIR-12 QA Lab-2

ABSTRACT This paper describes a multi-choice question answering system we designed for the . This system aims at analysing and answering world history multi-choice questions in the Japanese National Center Test (in English). Our system utilizes preliminary results from an information retrieval baseline as a starting point, and improves by taking structured knowledge base as well as additional time constraints into consideration. In the final evaluation, we achieved 34 points on the 2011 test dataset. Team Name WIP Subtasks National Center Tests, Formal Run (English