A major challenge in representation learning for NLP is to produce models that are robust to dataset biases.
A major challenge in representation learning for NLP is to produce models that are robust to dataset biases. In a recent work published by a team of Hugging Face and Cornell University researchers, the authors have explored the notion of how models with limited capacity primarily learn to exploit biases in the dataset Read more: https://analyticsindiamag.com/can-ml-models-eliminate-bias-from-datasets-on-their-own/
Researchers Claim Inconsistent Model Performance In Most ML Research. The process of benchmarking is considered to be one of the most crucial assets for the progress of AI and machine learning research.
Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge.NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition
NLP Researchers from Merck Group have developed an NLP-based search engine to find accurate COVID actionable insights.
Natural language processing (NLP) has made several remarkable breakthroughs in recent years by providing implementations.
NLP Profiler is a simple NLP library which works on profiling of textual datasets with one one more text columns and provides high-level insights.