all AI news
Where's the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit. (arXiv:2208.04749v3 [cs.CL] UPDATED)
Oct. 25, 2022, 1:19 a.m. | Mughilan Muthupari, Samrat Halder, Asad Sayeed, Yuval Marton
cs.CL updates on arXiv.org arxiv.org
Observing that for certain NLP tasks, such as semantic role prediction or
thematic fit estimation, random embeddings perform as well as pretrained
embeddings, we explore what settings allow for this and examine where most of
the learning is encoded: the word embeddings, the semantic role embeddings, or
``the network''. We find nuanced answers, depending on the task and its
relation to the training objective. We examine these representation learning
aspects in multi-task learning, where role prediction and role-filling are
supervised …
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Research Assistant/Associate, Health Data Science [LKCMedicine]
@ Nanyang Technological University | NTU Novena Campus, Singapore
Senior Machine Learning Engineer, Portfolio ML
@ Affirm | Remote Canada
[Sessional Lecturer] Foundations of Data Analytics and Machine Learning - APS1070
@ University of Toronto | Toronto, ON, CA
Senior Data Scientist
@ Prosper | United States
Data Analyst
@ ZF Friedrichshafen AG | Coimbatore, TN, IN, 641659