Natural Language Processing Advancements By Deep Learning: A Survey
원문 초록
Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia
Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech
Towards Temporally Explainable Dysarthric Speech Clarity Assessment
Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit Poetry
BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation System
Exploring Generative Error Correction for Dysarthric Speech Recognition
토론
이 논문과 연결된 포럼 글이 아직 없습니다.