The ability of conversational query understanding (CQU) is indispensable to multi-turn QA. However, existing methods are data-driven and expensive to extend to new conversation domains, or under specific frameworks and hard to apply to other underlying QA technologies. We propose a novel contextual query reformulation (CQR) module based on reformulation actions for general CQU. The actions are domain-independent and scalable, since they capture syntactic regularities of conversations. For action generation, we propose a multi-task learning framework enhanced by coreference resolution, and introduce grammar constraints into the decoding process. Then CQR synthesizes standalone queries based on the actions, which naturally adapts to original downstream technologies. Experiments on different CQU datasets suggest that action-based methods substantially outperform direct reformulation, and the proposed model performs the best among the methods.

MOTIVATION: Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures. RESULTS: We report a new Artificial Intelligence-based protein structure Refinement method called AIR. Its fundamental idea is to use multiple energy functions as multi-objectives in an effort to correct the potential inaccuracy from a single function. A multi-objective particle swarm optimization algorithm-based structure refinement is designed, where each structure is considered as a particle in the protocol. With the refinement iterations, the particles move around. The quality of particles in each iteration is evaluated by three energy functions, and the non-dominated particles are put into a set called Pareto set. After enough iteration times, particles from the Pareto set are screened and part of the top solutions are outputted as the final refined structures. The multi-objective energy function optimization strategy designed in the AIR protocol provides a different constraint view of the structure, by extending the one-dimension optimization to a new three-dimension space optimization driven by the multi-objective particle swarm optimization engine. Experimental results on CASP11, CASP12 refinement targets and blind tests in CASP 13 turn to be promising. AVAILABILITY AND IMPLEMENTATION: The AIR is available online at: www.csbio.sjtu.edu.cn/bioinf/AIR/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.