In this paper, we present the VHED (VIST Human Evaluation Data) dataset, which first re-purposes human evaluation results for automatic evaluation; hence we develop Vrank (VIST Ranker), a novel reference-free VIST metric for story evaluation.
We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase.
An engaging and provocative question can open up a great conversation.
In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations.
Ranked #1 on Question Answering on FinQA
We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data.