5 accepted papers
We propose a novel framework for data-efficient black-box robot learning under constraints. Our approach integrates probabilistic inference with Lagrangian optimization. With the guide of a learned Gaussian process model, the Lagrange multiplier is controlled by the probability of whether the constr
We propose a novel framework for data-efficient black-box robot learning under constraints. Our approach integrates probabilistic inference with Lagrangian optimization. With the guide of a learned Gaussian process model, the Lagrange multiplier is controlled by the probability of whether the constr…
We tackle Event Argument Extraction (EAE) in the manner of template-based generation. Based on our exploration of generative EAE, it suffers from several issues, such as multiple arguments of one role, generating words out of context and inconsistency with prescribed format. We attribute it to the w…
Event Detection (ED) is a task of automatically extracting multi-class trigger words. The understanding of word sense is crucial for ED. In this paper, we utilize context-specific commonsense knowledge to strengthen word sense modeling. Specifically, we leverage a Context-specific Knowledge Selector…
We leverage cross-language data expansion and retraining to enhance neural Event Detection (abbr., ED) on English ACE corpus. Machine translation is utilized for expanding English training set of ED from that of Chinese. However, experimental results illustrate that such strategy actually results in…