Diversity constraints on Public Housing Allocation in Singapore
The state of Singapore employs a unique large-scale public housing program, accounting for over 80 percent of its residential real-estate. In addition to providing a social benefit to its citizens and permanent residents in the form of subsidized housing, Singapore uses its housing allocation program to ensure ethnic diversity in its neighborhoods; however, limiting people's ability to freely choose apartments incurs some welfare loss. Our work studies this problem via the computational economics lens.
2018-1 - 2018-7
Layered Explanations: Interpreting Neural Networks with Numerical Influence Measures
Deep learning is currently receiving considerable attention from the machine learning community due to its predictive power. However, its lack of interpretability raises numerous concerns. Since neural networks are deployed in high-stakes domains, stakeholders expect to receive acceptable human interpretable explanations. We explain the decisions of neural networks using layered explanations: we use influence measures in order to compute a numerical value for each layer. Using layerwise influence measures, we identify the layers that contain the most explanatory power, and use those to generate explanations.
2017-7 - 2018-12
Incorporating Modality chunking into Vietnamese-Korean Statistical Machine Translation system
Vietnamese and Korean are a language pair sharing many common semantic concepts that are exploitable and useful to develop a good statistical machine translation system. In light of this, I created a mapping table for modality concept, say, a verb can imply suggestion, politeness, or social position of speaker to listener; and incorporate it into the data preprocessing pipeline. This is a collaborative project of CLC lab, KLE lab, and SYSTRAN company.
2016-9 - 2016-11
Building Vietnamese WordNet-annotated corpus for advanced tasks in NLP
With available WordNet annotated corpus in English side, one can use aligments provided by GIZA toolkit to project the WordNet tags into Vietnamese side. Once this goal achieved, the new generated corpus is expected to contain many useful semantic information such that the SMT system could reach a better performance. The problem is that alignments vary in many forms: 1-1, 1-n, m-1, and m-n. Thus, we proposed some heuristics in combining ovelapped alignments in order to obtain the best projection result, which is then evaluated on a hand-labeled test set.