PhD Seminar • Artificial Intelligence • Robust Embeddings Via Distributions

Tuesday, September 28, 2021 1:00 pm - 1:00 pm EDT (GMT -04:00)

Please note: This PhD seminar will be given online.

Kira Selby, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Pascal Poupart

Despite recent monumental advances in the field, many Natural Language Processing (NLP) models still struggle to perform adequately on noisy domains. We propose a novel probabilistic embedding-level method to improve the robustness of NLP models. Our method, Robust Embeddings via Distributions (RED), incorporates information from both noisy tokens and surrounding context to obtain distributions over embedding vectors that can express uncertainty in semantic space more fully than any deterministic method. We evaluate our method on a number of downstream tasks using existing state-of-the-art models in the presence of both natural and synthetic noise, and demonstrate a clear improvement over other embedding approaches to robustness from the literature.

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