A Benchmark for Understanding and Generating Dialogue between Characters in Stories

Many classical fairy tales, fiction, and screenplays leverage dialogue to advance story plots and establish characters. We present the first study to explore whether machines can understand and generate dialogue in stories, which requires capturing traits of different characters and the relationships between them. To this end, we propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition, i.e., generating missing dialogue turns and predicting speakers for specified dialogue turns, respectively. We build a new dataset DialStory, which consists of 105k Chinese stories with a large amount of dialogue weaved into the plots to support the evaluation. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory. Furthermore, we propose to learn explicit character representations to improve performance on these tasks. Extensive experiments and case studies show that our approach can generate more coherent and informative dialogue, and achieve higher speaker recognition accuracy than strong baselines

Shielding Design and Dose Evaluation for HTR-PM Fuel Transport Pipelines by QAD-CGA Program

The spherical fuel elements are adopted in the high-temperature gas-cooled reactor pebble-module (HTR-PM). The fuel elements will be discharged continuously from the reactor core and transported into the fuel transport pipelines during the reactor operation, leading to spatially varying dose outside the pipeline. In this case, the dose evaluation faces two major challenges, including dynamic source terms and pipelines with varying lengths and shapes. This study tries to handle these challenges for HTR-PM through comprehensive calculations using the QAD-CGA program and to design the corresponding shielding of the pipeline. During the calculation, it is assumed that a spherical fuel element stays in different positions of the pipelines in turn, and the corresponding dose contributions were calculated. By integrating the dose contributions at different positions, the dose at the points of interest can be obtained. The total dose is further determined according to the assumed fuel elements transport speed of 5 m/s and total 6000 fuel elements transportation per day. Two types of fuel transport pipelines and two source terms were considered, i.e., the spent fuel element transport pipelines with corresponding spent fuel source term and the different burn-up fuel element transport pipelines with the average burn-up fuel source term. Doses at different points of interest were calculated with no shielding scenario and with lead shielding of different thicknesses scenario. To evaluate the shielding effect, the dose limit of the orange radiation zone of HTR-PM and the radiation damage thresholds from NCRP report No.51 were both adopted. The calculated results show that, for pipelines that transport the spent fuel, a 4 cm lead shielding will be enough. And for pipelines that transport fuel elements with different burn-up, a 5 cm lead shielding will be added. The method and results can provide valuable reference for other work of HTR-PM

EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training

Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems. However, previous works mainly focus on showing and evaluating the conversational performance of the released dialogue model, ignoring the discussion of some key factors towards a powerful human-like chatbot, especially in Chinese scenarios. In this paper, we conduct extensive experiments to investigate these under-explored factors, including data quality control, model architecture designs, training approaches, and decoding strategies. We propose EVA2.0, a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters, and make our models and code publicly available. To our knowledge, EVA2.0 is the largest open-source Chinese dialogue model. Automatic and human evaluations show that our model significantly outperforms other open-source counterparts. We also discuss the limitations of this work by presenting some failure cases and pose some future directions.Comment: 12 pages, 5 figures. The code and pre-trained models are publicly available at https://github.com/thu-coai/EV