Abstract: Systems, computer-implemented methods, and computer program products to facilitate reducing error propagation when combining generative aligners and transition-based aligners are provided. According to an embodiment, a system can comprise a processor that executed components stored in memory. The computer executable components comprise a generative alignment component, an error propagation component, a discriminative parser, and a stochastic oracle policy component. The error propagation component can compute a posterior distribution over one or more hard alignments of parts given a pair of the generative alignment component. The discriminative parser can be trained via the stochastic oracle policy component to reduce error propagation when combining the generative alignment component with the discriminative parser.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating interaction and network asset association predictions. Recommendations for network assets in response to interactions with other network assets may be provided. Initially, a pair of items, including a seed asset and a candidate asset, is received. Each word in the seed and candidate titles, each aspect, and the categories may be embedded into a k-dimensional vector space. The embedding may then be aggregated to construct an n-dimensional vector representing a seed asset and an n-dimensional vector representing a candidate asset which are used to determine and generate a probability that the seed asset and the candidate asset are contemporaneously operated upon by the same user. The system may then rank recommendation candidates by a co-interaction probability output of the neural network system.