Paraphrase Generation with Deep Reinforcement Learning

Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP), and plays a key role in a number of applications such as question answering, search, and dialogue. In this paper, we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose a new framework for the task, which consists of a \textit{generator} and an \textit{evaluator}, both of which are learned from data. The generator, built as a sequence-to-sequence learning model, can produce paraphrases given a sentence. The evaluator, constructed as a deep matching model, can judge whether two sentences are paraphrases of each other. The generator is first trained by deep learning and then further fine-tuned by reinforcement learning in which the reward is given by the evaluator. For the learning of the evaluator, we propose two methods based on supervised learning and inverse reinforcement learning respectively, depending on the type of available training data. Empirical study shows that the learned evaluator can guide the generator to produce more accurate paraphrases. Experimental results demonstrate the proposed models (the generators) outperform the state-of-the-art methods in paraphrase generation in both automatic evaluation and human evaluation.Comment: EMNLP 201

Reinforcement Learning based NLP

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 in order 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 used for a variety of linguistic problems, including text summarization, conversation systems, and machine translation. Sequence-to- sequence Two common methods used in RL-based NLP are reinforcement learning and deep reinforcement learning. Sequence-to-sequence While deep reinforcement learning includes training a neural network to discover the optimum strategy for a language challenge, reinforcement learning (RL) trains a model to create a series of words or characters that most closely matches a goal sequence. In several linguistic challenges, RL-based NLP has demonstrated promising results and attained cutting-edge performance. 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 potential line of inquiry for NLP research in the future. This method outperforms more established NLP strategies in a variety of language problems and has the added benefit of being able to improve over time with user feedback. To further enhance RL-based NLP's effectiveness and increase its applicability to real-world settings, future research should concentrate on resolving the difficulties associated with this approach.Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved