ganeshjawahar/drl4nlp.scratchpad
Notes on Deep Reinforcement Learning for Natural Language Processing papers
This project contains summarized notes on research papers that explore how machines can learn to understand and generate human language through trial and error, similar to how humans learn. It processes academic papers and provides concise overviews of methodologies for tasks like abstractive summarization, query reformulation, and dialogue agent development. Researchers and developers working on advanced natural language processing applications will find this useful.
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Use this if you are a researcher or developer who wants to quickly grasp the core ideas and approaches from key papers in deep reinforcement learning for natural language processing.
Not ideal if you are looking for ready-to-use code, tutorials, or a high-level, non-technical introduction to NLP.
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Jul 17, 2017
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