Jessica Lundin


2021

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Multi-Pair Text Style Transfer for Unbalanced Data via Task-Adaptive Meta-Learning
Xing Han | Jessica Lundin
Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing

Text-style transfer aims to convert text given in one domain into another by paraphrasing the sentence or substituting the keywords without altering the content. By necessity, state-of-the-art methods have evolved to accommodate nonparallel training data, as it is frequently the case there are multiple data sources of unequal size, with a mixture of labeled and unlabeled sentences. Moreover, the inherent style defined within each source might be distinct. A generic bidirectional (e.g., formal informal) style transfer regardless of different groups may not generalize well to different applications. In this work, we developed a task adaptive meta-learning framework that can simultaneously perform a multi-pair text-style transfer using a single model. The proposed method can adaptively balance the difference of meta-knowledge across multiple tasks. Results show that our method leads to better quantitative performance as well as coherent style variations. Common challenges of unbalanced data and mismatched domains are handled well by this method.

2017

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An Insight Extraction System on BioMedical Literature with Deep Neural Networks
Hua He | Kris Ganjam | Navendu Jain | Jessica Lundin | Ryen White | Jimmy Lin
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.