Tengfei Ma

Learning Crosslingual Word Embeddings without Bilingual Corpora

2016 · arXiv (Cornell University)

Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools. However, previous attempts had expensive resource requirements, difficulty incorporating monolingual data or were unable to handle …

FastGCN: Fast Learning with Graph Convolutional Networks via Importance\n Sampling

2018 · arXiv (Cornell University)

The graph convolutional networks (GCN) recently proposed by Kipf and Welling\nare an effective graph model for semi-supervised learning. This model, however,\nwas originally designed to be learned with the presence of both training and\ntest data. Moreover, …

Pre-Training BERT on Domain Resources for Short Answer Grading

2019

Chul Sung, Tejas Dhamecha, Swarnadeep Saha, Tengfei Ma, Vinay Reddy, Rishi Arora. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). …

IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research

2023 · arXiv (Cornell University)

Graph neural networks (GNNs) have shown high potential for a variety of real-world, challenging applications, but one of the major obstacles in GNN research is the lack of large-scale flexible datasets. Most existing public datasets …

Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction

2023 · PubMed

Class imbalance issues are prevalent in the medical field and significantly impact the performance of clinical predictive models. Traditional techniques to address this challenge aim to rebalance class proportions. They generally assume that the rebalanced …

AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection

2023 · arXiv (Cornell University)

Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention. Modern software often involves a diverse range of programming languages. However, current code clone detection methods …