RI-Medium: Collaborative Research: Dynamically-Structured Conditional Random Fields for Complex, Natural Domains

生物信息学、自然语言理解、计算机视觉、信息检索和其他领域的最新进展在很大程度上得益于条件随机场(CRF)-序列、树和网格等结构化输出的机器学习模型。然而，这些应用领域中的许多基本问题不仅涉及固定的结构，而且涉及必须推断的结构。这种结构上的歧义产生于不同表示级别上的交互选择(例如，从字符序列到含义，或者从像素到场景解释)。该项目将条件随机场(CRF)从固定的图形结构转移到在推理过程中动态构建的结构。这种能力将是构建下一代系统的关键，这些系统不仅能解决单个问题，而且能以统一的方式解决自然语言理解和计算机视觉中的复杂多步问题。

Recent progress in bioinformatics, natural language understanding, computer vision, information retrieval and other areas has been significantly enabled by "conditional random fields" (CRFs)---machine learning models of structured outputs, such as sequences, trees and grids. However, many of the fundamental problems in these application areas involve not just fixed structures, but structures that must be inferred. This structural ambiguity arises from interacting choices at different levels of representation (e.g. from character sequences to meaning, or from pixels to scene interpretation). The project will move conditional random fields (CRFs) beyond fixed graphical structures to structures that are constructed dynamically during inference. Such a capability will be key to building next-generation systems that solve, not just an individual piece of a problem, but complex multi-step problems, as found in natural language understanding and computer vision, in a unified way.