NLP303 - Natural Language Processing and Speech Recognition - IT Assignment Help

Assignment Task

Learning Outcomes - The Subject Learning Outcomes demonstrated by successful completion of the task below include:
a) Evaluate Natural Language Processing and Speech Recognition techniques.
b) Apply the theories and the frameworks of Natural Language Processing and Speech Recognition.
d) Develop solutions to Natural Language Processing and Speech Recognition problems

Assessment Task
This assessment is about exploring and investigating the use of the Hugging Face (HFace) opensource Transformers library. You will demonstrate the value of the library through high-level Natural Language Processing (NLP) tasks and delivering NLP solutions.
Please refer to the Task Instructions for details on how to complete this task.

Context
This assessment recognises the tectonic shift taking place when approaching NLP projects in the 2020s. According to Sebastian Ruder, key global thought leader and research scientist in NLP with Google DeepMind, “It only seems to be a question of time until pretrained word embeddings will be dethroned and replaced by pretrained language models in the toolbox of every NLP practitioner” (Ruder, 2018). Together with the overwhelming demonstrations and transformer technology use by OpenAI, Google, Facebook, Microsoft and Baidu, no reason at all exists for not considering this technology as part of any reasonably sized NLP projects (i.e., beyond toy) applications or research.
Understanding transformer technology and implementations is essential for anyone who works in the NLP field. Therefore, this transformer-centric assessment is essential to undertake in order to:
1. Help provide a context to keep up with the continual news updates on transformers, and
2. Enter the workplace in any role associated with NLP if only to compare and contrast the new methods of conducting NLP tasks with the traditional, fragile, rules-based approach.

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