Semantic-based Pre-training for Dialogue Understanding

Pre-trained language models have made great progress on dialogue tasks. However, these models are typically trained on surface dialogue text, thus are proven to be weak in understanding the main semantic meaning of a dialogue context. We investigate Abstract Meaning Representation (AMR) as explicit semantic knowledge for pre-training models to capture the core semantic information in dialogues during pre-training. In particular, we propose a semantic-based pre-training framework that extends the standard pre-training framework (Devlin et al., 2019) by three tasks for learning 1) core semantic units, 2) semantic relations and 3) the overall semantic representation according to AMR graphs. Experiments on the understanding of both chit-chats and task-oriented dialogues show the superiority of our model. To our knowledge, we are the first to leverage a deep semantic representation for dialogue pre-training.Comment: Accepted as oral in COLING202

Dynamic modeling issues for power system applications

Power system dynamics are commonly modeled by parameter dependent nonlinear differential-algebraic equations (DAE) x p y x f ) and 0 = p y x g ) . Due to (,, (,, the algebraic constraints, we cannot directly perform integration based on the DAE. Traditionally, we use implicit function theorem to solve for fast variables y to get a reduced model in terms of slow dynamics locally around x or we compute y numerically at each x . However, it is well known that solving nonlinear algebraic equations analytically is quite difficult and numerical solution methods also face many uncertainties since nonlinear algebraic equations may have many solutions, especially around bifurcation points. In this thesis, we apply the singular perturbation method to model power system dynamics in a singularly perturbed ODE (ordinary-differential equation) form, which makes it easier to observe time responses and trace bifurcations without reduction process. The requirements of introducing the fast dynamics are investigated and the complexities in the procedures are explored. Finally, we propose PTE (Perturb and Taylors expansion) technique to carry out our goal to convert a DAE to an explicit state space form of ODE. A simplified unreduced Jacobian matrix is also introduced. A dynamic voltage stability case shows that the proposed method works well without complicating the applications

Political connection and business transformation in family firms:evidence from China

We investigate the impact of family ownership on core business transformation and the moderating role of political connections in this relation through a Probit model, conditional Logit model, and Heckman selection model with instrumental variable using data from Chinese listed companies covering 2001–2010. The results demonstrate that, compared with non-family firms, family firms are more likely to transform their core business, enter strongly correlative industries and non-regulated industries, and adopt a mergers and acquisitions (M&A) mode. Furthermore, compared with politically non-connected family firms, family firms with political connections are more likely to conduct business transformation and adopt M&A rather than an internal cultivation mode to realize transformation. In addition, political connections make family firms more likely to enter weakly correlative industries and increase their chances of entering government-regulated industries