Summary of Enhancing Text Generation in Joint Nlg/nlu Learning Through Curriculum Learning, Semi-supervised Training, and Advanced Optimization Techniques, by Rahimanuddin Shaik et al.
Enhancing Text Generation in Joint NLG/NLU Learning Through Curriculum Learning, Semi-Supervised Training, and Advanced Optimization Techniques
by Rahimanuddin Shaik, Katikela Sreeharsha Kishore
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for improving text generation in joint Natural Language Generation (NLG) and Natural Language Understanding (NLU) learning is presented, which leverages transformer-based encoders and decoders, pre-trained language models like Optimized BERT, and hybrid architectures. The model incorporates techniques such as reinforcement learning with policy gradient, semi-supervised training, improved attention mechanisms, and differentiable approximations to handle complex linguistic tasks effectively. Feature extraction methods including POS tagging, Bag of words, and Term Frequency-Inverse Document Frequency (TF-IDF) are applied, along with pre-processing steps like cleaning, tokenization, stemming, and stop-word removal. The proposed model is implemented using Python. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores a new way to make computers generate text that makes sense and is relevant to the conversation. Right now, computer-generated text can sound fake or awkward because it’s hard for machines to understand what humans want to say. The scientists behind this project created a special system that combines two important skills: generating text and understanding language. They used powerful tools like pre-trained language models and advanced algorithms to make their system work better. By combining these technologies, they were able to create a model that can generate complex linguistic tasks effectively. |
Keywords
» Artificial intelligence » Attention » Bag of words » Bert » Feature extraction » Language understanding » Reinforcement learning » Semi supervised » Stemming » Text generation » Tf idf » Tokenization » Transformer
