Recent years have witnessed the rapid development of concept map generation techniques due to their advantages in providing well-structured summarization knowledge from free texts. Traditional unsupervised methods do not generate task-oriented maps, whereas deep generative models require large amounts training data. In this work, we present GT-D2G (Graph Translation-based Document To Graph), an...

Taskflow aims to streamline the building of parallel and heterogeneous applications using a lightweight task graph-based approach. introduces an expressive graph programming model assist developers in implementation decomposition strategies on computing platform. Our distinguishes itself as very general class parallelism with in-graph control flow enable end-to-end optimization. To support our ...

Text coherence evaluation becomes a vital and lovely task in Natural Language Processing subfields, such as text summarization, question answering, text generation and machine translation. Existing methods like entity-based and graph-based models are engaging with nouns and noun phrases change role in sequential sentences within short part of a text. They even have limitations in global coheren...

We extend hierarchical task network planning with task insertion (TIHTN) by introducing state constraints, called TIHTNS. We show that just as for TIHTN planning, all solutions of the TIHTNS planning problem can be obtained by acyclic decomposition and task insertion, entailing that its planexistence problem is decidable without any restriction on decomposition methods. We also prove that the e...