UniLM: A Unified Pre-Trained Language Model for NLU and NLG Tasks
UniLM is a new benchmark for Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks. It is a unique language model that uses a shared Transformer network pre-trained on unidirectional, bidirectional, and sequence-to-sequence tasks, with special self-attention masks for contextual prediction control. This approach sets UniLM apart from other models like BERT, and has led to its superior performance in the GLUE benchmark and in SQuAD 2.0 and CoQA question answering. UniLM has also set new records in five NLG datasets, including significant improvements in tasks like CNN/DailyMail and Gigaword summarization.
UniLM’s models and code have been made available to the research community, allowing for further advancements in NLU and NLG. This tool has the potential to revolutionize the way we interact with language, improving tasks like machine translation, text summarization, and sentiment analysis. With UniLM, users can achieve more accurate and efficient results in their language-based projects.
