Short text Matching plays an important role in natural language processing tasks such as information retrieval (IR), question answering (QA), and dialogue system. Traditional text matching methods rely on human-crafted rules and template. Though they are effective to deal with specific situations, but lack in the ability to handle unobserved cases. In natural language, it’s infeasible to exhaust every possible expression that conveys the same ideas. Recently, neural network-based models have good generalizability to unobserved cases but provide less interpretability. In this project, we aim to balance the two sides by introducing the concept of entity to QA. We propose an interpretable, deep learning-based short text matching model for customer service domain question answering, which consists of two major components: Word segmentation enhanced Named Entity Recognition (NER), and text semantic matching. By doing so, the proposed method explicitly models entities and types of entity as building blocks of interpretable QA.
The goal of this project is to design and develop an intelligent task management for online education and business purpose. The project includes OneTask online platform, self-adaptive neural question generation for study quality evaluation and companion, GCN-based DRL model for self-adaptive neural question generation and study content recommendation and entity-related open-domain question answering system.
We propose the ModelKB system automating end-to-end model management in deep learning. We will develop a ModelKB prototype that can automatically (1) extract and store the model’s metadata-including its architecture, weights, and configuration; (2) visualize, query, and compare experiments; and (3) reproduce experiments.
DeepCloud is designed as an open software-defined ecosystem for researchers at different levels with the following salient features and transformative impacts. It is one of the first massively scalable multi-tenant open cloud platform with full-fledged building blocks and comprehensive shared stores (app, model, knowledge, data) for deep learning research and applications.
The goals of this project are to design and develop an Intelligent Task Management Platform with task-oriented assistants for commercial and education services, design and develop an open-domain question answering system, and design and develop a deep neural model for question answering with external Knowledge Base.
The objectives of this project involve developing a customer service ontology represented as knowledge graph and building an automatic customer service system backed by the knowledge graph.
The objective of this project is to create data-driven deep learning models that (1) optimizes the institutional investor’s investment strategy in the stock market (2) provides decision support for stock pick 93) are “accountable” and (4) are flexible enough to properly accommodate heterogeneous data.
The goal of this projects is to prototype to segment 3D objects into equivalence classes using known Deep Learning techniques; understand performance of known techniques.