13 citations · 34 across the 7 of their papers we have counts for
7 papers
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
Mingbin Xu, Congzheng Song, Ye Tian +10
Federated Learning (FL) is a technique to train models using data distributed across devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our…
Effective Context and Fragment Feature Usage for Named Entity Recognition
Nargiza Nosirova, Mingbin Xu, Hui Jiang
In this paper, we explore a new approach to named entity recognition (NER) with the goal of learning from context and fragment features more effectively, contributing to the improv…
A Multi-task Learning Approach for Named Entity Recognition using Local Detection
Nargiza Nosirova, Mingbin Xu, Hui Jiang
Named entity recognition (NER) systems that perform well require task-related and manually annotated datasets. However, they are expensive to develop, and are thus limited in size.…
A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases
Dekun Wu, Nana Nosirova, Hui Jiang +1
Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One popular way to solve the KB-QA problem is to make use of a pipeline of sever…
Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation
Xi Zhu, Mingbin Xu, Hui Jiang
In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem. FOFE enables us to encode variab…
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
Mingbin Xu, Hui Jiang
In this paper, we study a novel approach for named entity recognition (NER) and mention detection in natural language processing. Instead of treating NER as a sequence labelling pr…