Research

Neural Question Generation for Reading Comprehension

Deep Learning for Fine-grained Opinion Extraction

Research intern at Cornell NLP group, supervised by Prof. Claire Cardie.

• Proposed to evaluate labeling sequences using sentence-level log-likelihood (SLL) at output layer of deep recurrent neural networks. Empirical results showed around 4% improvement on F-measure of opinion target extraction and more accurate n-best ranking for labeling sequences.

• Proposed to use deep recurrent neural networks to produce the n-best labeling sequences which can be fed into integer linear programming (ILP) system for joint inference.

• Designed heuristic rules using dependency parse tree to eliminate inappropriate opinion candidates during inference. Empirical results showed higher precision and higher F-measure.

• Building an ensemble system using the above algorithm to participate in Belief and Sentiment Evaluation 2015 (BeSt2015).

Online Auction Mechanism Design with Time-varying Value

Research assistant at Advanced Network Laboratory, Shanghai Jiao Tong University

• Proposed the new scenario in online auction mechanism design where agent’s value may vary over time.

• Extended the classic payment determination algorithm (Myerson) to fit the new model. Proposed mechansim ensured strategy-proofness and achieved constant competitive ratio.