Minnesota Natural Language Processing Seminar Series: Pushing the Boundary of Unsupervised Text Generation
The Minnesota Natural Language Processing (NLP) Seminar is a venue for faculty, postdocs, students, and anyone else interested in theoretical, computational, and human-centric aspects of natural language processing to exchange ideas and foster collaboration. The talks are every other Friday from 12 p.m. - 1 p.m. during the Fall 2021 semester.
This week's speaker, Philippe Laban (Salesforce Research), will be giving a talk titled "Pushing the Boundary of Unsupervised Text Generation."
Recent progress in automated text generation relies predominantly on the use of large datasets, sometimes requiring millions of examples for each application setting. In the first part of the talk, we'll develop novel text generation methods that balance the goals of fluency, consistency, and relevancy without requiring any training data. We focus on text summarization and simplification by directly defining a multi-component reward, and training text generators to optimize this objective. The novel approaches that we introduce perform better than all existing unsupervised approaches and in many cases outperform those that rely on large datasets, showing that high-performing NLP models are possible when little data is available.
Philippe Laban is a Research Scientist at Salesforce Research, where he works on text generation projects, including summarization and interactive question answering. Previously, he obtained his Ph.D. from UC Berkeley, where he was advised by Marti Hearst and John Canny. His work in Berkeley focused on designing unsupervised methods for text generation and on building and adapting NLP techniques to a very large, noisy and evolving news dataset. He did his undergraduate education at Georgia Tech, doing research in signal processing and discrete mathematics.