Word Representation
Word representation in natural language processing focuses on creating numerical vectors that capture the meaning and context of words, enabling computers to understand and process human language. Current research emphasizes improving contextualized word embeddings, often using transformer-based architectures like BERT, to generate dynamic representations that change based on the surrounding text, and exploring methods to enhance their robustness and efficiency, including techniques like soft-prompt tuning and novel pooling strategies. These advancements are crucial for improving performance in various downstream tasks such as text classification, machine translation, and question answering, ultimately impacting the development of more sophisticated and accurate natural language processing systems.
Papers
Human Gait Recognition Using Bag of Words Feature Representation Method
Nasrin Bayat, Elham Rastegari, Qifeng Li
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
Sebastian Hofstätter, Omar Khattab, Sophia Althammer, Mete Sertkan, Allan Hanbury
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models
Mark Chu, Bhargav Srinivasa Desikan, Ethan O. Nadler, D. Ruggiero Lo Sardo, Elise Darragh-Ford, Douglas Guilbeault
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
Lihu Chen, Gaël Varoquaux, Fabian M. Suchanek