Reinforcement Learning based NLP
Gopi Krishna
Gopi Krishna, B Tech, Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India.
Manuscript received on 04 September 2023 | Revised Manuscript received on 15 September 2023 | Manuscript Accepted on 15 September 2023 | Manuscript published on 30 September 2023 | PP: 1-4 | Volume-13 Issue-4, September 2023 | Retrieval Number: 100.1/ijsce.J047610101023 | DOI: 10.35940/ijsce.J0476.0913423
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the field of Natural Language Processing (NLP), reinforcement learning (RL) has drawn attention as a viable method for training models. An agent is trained to interact with a linguistic environment to carry out a given task using RL- based NLP, and the agent learns from feedback in the form of rewards or penalties. This method has been effectively applied to a variety of linguistic problems, including text summarisation, conversational systems, and machine translation. Sequence-tosequence Two common methods used in RL-based NLP are reinforcement learning and deep reinforcement learning. Sequence-to-sequence. While deep reinforcement learning involves training a neural network to discover the optimal strategy for a language challenge, reinforcement learning (RL) trains a model to generate a sequence of words or characters that most closely match a given goal sequence. In several linguistic challenges, RL-based NLP has demonstrated promising results, achieving cutting-edgeperformance. There are still issues to be solved, such as the need for more effective exploration tactics, data scarcity, and sample efficiency. In summary, RL-based NLP represents a promising avenue for future research in NLP. This method outperforms more established NLP strategies in various language problems and has the added benefit of being able to improve over time with user feedback. To further enhance the effectiveness and applicability of RL-based NLP in real-world settings, future research should focus on addressing the challenges associated with thisapproach.
Keywords: RL, NLP, AI
Scope of the Article: Artificial Intelligence