Xuezhe Ma

Choosing Transfer Languages for Cross-Lingual Learning

2019 · arXiv (Cornell University)

Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages. However, …

Evaluating Large Language Models on Controlled Generation Tasks

2023 · arXiv (Cornell University)

While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc, there have been few studies looking into the controllability of large …

Harnessing Deep Neural Networks with Logic Rules

2016 · arXiv (Cornell University)

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs …

An Interpretable Knowledge Transfer Model for Knowledge Base Completion

2017 · arXiv (Cornell University)

Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, \emph{ITransF}, to perform knowledge base completion. Equipped with a sparse attention mechanism, …

On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing

2019

Wasi Ahmad, Zhisong Zhang, Xuezhe Ma, Eduard Hovy, Kai-Wei Chang, Nanyun Peng. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and …

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

2016

State-of-the-art sequence labeling systems traditionally require large amounts of taskspecific knowledge in the form of handcrafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word-and …