Lara McConnaughey


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Scaling Language Model Size in Cross-Device Federated Learning
Jae Ro | Theresa Breiner | Lara McConnaughey | Mingqing Chen | Ananda Suresh | Shankar Kumar | Rajiv Mathews
Proceedings of the First Workshop on Federated Learning for Natural Language Processing (FL4NLP 2022)

Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage various techniques for mitigating these bottlenecks to train larger language models in cross-device federated learning. With systematic applications of partial model training, quantization, efficient transfer learning, and communication-efficient optimizers, we are able to train a 21M parameter Transformer that achieves the same perplexity as that of a similarly sized LSTM with ∼10× smaller client-to-server communication cost and 11% lower perplexity than smaller LSTMs commonly studied in literature.


The Labeled Segmentation of Printed Books
Lara McConnaughey | Jennifer Dai | David Bamman
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We introduce the task of book structure labeling: segmenting and assigning a fixed category (such as Table of Contents, Preface, Index) to the document structure of printed books. We manually annotate the page-level structural categories for a large dataset totaling 294,816 pages in 1,055 books evenly sampled from 1750-1922, and present empirical results comparing the performance of several classes of models. The best-performing model, a bidirectional LSTM with rich features, achieves an overall accuracy of 95.8 and a class-balanced macro F-score of 71.4.