Cultivating Contemplative Mind in the Classroom

In Fall 2019, we showed video interviews of successful (i.e., graduated) alumni to first-year seminar students in the hope that incoming students would be inspired to adopt similar success strategies leading to increased retention and completion of their UNLV degree. The Academic Success Center filmed interviews with ten UNLV graduates who took our first-year seminar, COLA 100E. These COLA 100E Success Stories were then edited into three videos, each focusing on a particular theme, such as the first-year transition, the major selection process, and the key tips for graduation. The goal was that these successfully-graduated students would serve as motivational role models for UNLV’s diverse first-year student population. Though the alumni echoed concepts taught in the class, we imagined these peers would be more relatable than the instructor alone, encouraging students to identify with and potentially adopt new approaches to and perspectives of success early in their college careers.https://digitalscholarship.unlv.edu/btp_expo/1090/thumbnail.jp

Non-linear Learning for Statistical Machine Translation

Modern statistical machine translation (SMT) systems usually use a linear combination of features to model the quality of each translation hypothesis. The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit model on the current data. In this paper, we propose a non-linear modeling for the quality of translation hypotheses based on neural networks, which allows more complex interaction between features. A learning framework is presented for training the non-linear models. We also discuss possible heuristics in designing the network structure which may improve the non-linear learning performance. Experimental results show that with the basic features of a hierarchical phrase-based machine translation system, our method produce translations that are better than a linear model.Comment: submitted to a conferenc