We investigate properties of ABA+, a formalism that extends the well studied structured argumentation formalism Assumption-Based Argumentation (ABA) with a preference handling mechanism. In particular, we establish desirable properties that ABA+ semantics exhibit. These pave way to the satisfaction by ABA+ of some (arguably) desirable principles of preference handling in argumentation and nonmonotonic reasoning, as well as non-monotonic inference properties of ABA+ under various semantics.
This paper presents a supervised bayesian approach to model expertise in online forums with application to question routing. The proposed method extends the well-known sLDA model to the multi-task case, accounting for a supervised stage with multiple outputs per document corresponding to the users of the system. A study of the characteristics of real world data revealed a number of challenges in the practical application of this model, relevant to the research community.
But for a growing number of people, it's a mystery based not in the vagaries of human emotion but rather in the ones and zeros of proprietary software. According to an April 5 industry trend report focusing on voice-activated systems like Google Home and Amazon Echo, users are getting more than unsolicited Beauty and the Beast ads out of their digital assistants. That's right, people these days are intimately connecting to the Alexas of the world -- or at least fantasizing about doing so, anyway. "Over a third (37%) of regular voice technology users say that they love their voice assistant so much that they wish it were a real person," reads the report. "Even more astonishing is that more than a quarter of regular voice technology users say they have had a sexual fantasy about their voice assistant."
Zhang, Yuanzhe (Institute of Automation, Chinese Academy of Sciences) | He, Shizhu (Institute of Automation, Chinese Academy of Sciences) | Liu, Kang (Institute of Automation, Chinese Academy of Sciences) | Zhao, Jun (Institute of Automation, Chinese Academy of Sciences)
As the amount of knowledge bases (KBs) grows rapidly, the problem of question answering (QA) over multiple KBs has drawn more attention. The most significant distinction between multiple KB-QA and single KB-QA is that the former must consider the alignments between KBs. The pipeline strategy first constructs the alignments independently, and then uses the obtained alignments to construct queries. However, alignment construction is not a trivial task, and the introduced noises would be passed on to query construction. By contrast, we notice that alignment construction and query construction are interactive steps, and jointly considering them would be beneficial. To this end, we present a novel joint model based on integer linear programming (ILP), uniting these two procedures into a uniform framework. The experimental results demonstrate that the proposed approach outperforms state-of-the-art systems, and is able to improve the performance of both alignment construction and query construction.