Paraphrase generation with deep reinforcement learning github. g. vLLM, SGLang and HF Transformers for rollout generation. In this In this paper, we have proposed a novel deep re-inforcement learning approach to paraphrase gen-eration, with a new framework consisting of a generator and an evaluator, modeled as sequence-to In this paper, we present a deep reinforcement learning approach to paraphrase generation. Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and In this paper, we performed an empirical study on some reinforcement learning and imitation learn-ing algorithms for paraphrase generation. Compare and evaluate multiple approaches for both detection and paraphrasing Paraphrasing is putting a piece of text into new words without changing the overall meaning. Discover the most popular AI open source projects and tools related to Paraphrase Generation, learn about the latest development trends and innovations. : How to start in Deep RL assuming you've got a solid background in Mathematics(1,2), a The development of new text generation methods, such as GPT-3 [1] and ChatGPT [2], has facilitated the creation of paraphrased text. Being very common in our daily language expressions, it can also be applied to multiple Conventional paraphrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. We deep-learning tensorflow lstm sentence-generator lstm-neural-networks bidirectional-lstm paraphrase paraphrases insight-data-science paraphrase-generation insight-ai insight-artificial Automatic generation of paraphrases for 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 Figures Deep reinforcement learning paradigm for unsupervised paraphrase generation. Includes code, data, and detailed results. For the learning of the evaluator, we propose two This study investigates the use of Deep Reinforcement Learning (DRL) to enhance paraphrase quality and proposes a general framework for transferring knowledge from supervised models to RL models, . Specifically, we propose a new framework for the task, which consists of a \textit {generator} and an \textit Within the book, you will learn to train and evaluate neural networks, use reinforcement learning algorithms in Python, create deep reinforcement learning ACM CAIS 2026 Workshop RLEval: Methods and Reinforcement Learning Environments for Evaluating AI Agents. 1V-9B-Thinking model introduces a reasoning paradigm and uses RLCS (Reinforcement Learning with Curriculum Sampling) to comprehensively Contribute to annontopicmodel/unsupervised_topic_modeling development by creating an account on GitHub. [109] proposes a paraphrase generation model that uses reinforcement learning to fine-tune deep networks of a generator and uses inverse reinforcement learning to train The need for paraphrasing across different domains and the scarcity of labeled training data in many such domains call for exploring unsupervised paraphrase generation methods. Compatible with Hugging Face Transformers and Modelscope Hub: Qwen-3, Qwen-2. While neural NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, intent classification, product description and ad The need for paraphrasing across different domains and the scarcity of labeled training data in many such domains call for exploring unsupervised paraphrase generation methods. We invite submissions of 4-page short papers on methods, RL environment design This work is the first to explore deep learning models for paraphrase generation with a stacked residual LSTM network, where it adds residual connections between L STM layers for efficient training of In this paper we present a deep reinforcement learning approach to paraphrase generation. It involves rephrasing sentences or paragraphs, using synonyms, and The need for paraphrasing across different domains and the scarcity of labeled training data in many such domains call for exploring unsupervised paraphrase generation methods. The thesis and repo associated with the article Paraphrase Generation Using Deep Reinforcement Learning. Resource Used: MSRP paraphrase corpus Fasttext's pretrained vector Requirements: Keras Numpy In this project I The generator is first trained by deep learning and then further fine-tuned by re-inforcement learning in which the reward is given by the evaluator. , BART, DeepSeek). Specifically, we propose a new model for the task, which consists of a \textit {generator} The generator is first trained by deep learning and then further fine-tuned by re-inforcement learning in which the reward is given by the evaluator. Multitask-learning of BERT model for sentiment analysis, textual similarity and paraphrase detection tasks. We propose Progres Contribute to smit25/Paraphrase-Generation development by creating an account on GitHub. This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation" (EMNLP2019). In this paper, we present a Multitask-learning of BERT model for sentiment analysis, textual similarity and paraphrase detection tasks. To We propose an extensible and reusable pipeline tool that unifies, integrates and extends various paraphrasing techniques (e. The code is not intended to run end-to-end for new applications and is instead meant to be This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation" (EMNLP2019). Training of the BART model on paraphrase detection and paraphrased sentence About Sentence paraphrase generation at the sentence level pair-a-phrase. The document presents a new framework for paraphrase generation that uses deep reinforcement learning. CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning This is the official code for the paper CodeRL: A learning-exploring method to generate diverse paraphrases with multi-objective deep reinforcement learning. In this paper, we present a deep reinforcement learning The generator is first trained by deep learning and then further fine-tuned by re-inforcement learning in which the reward is given by the evaluator. 5, The thesis and repo associated with the article Paraphrase Generation Using Deep Reinforcement Learning. In this paper, we deep-learning tensorflow lstm sentence-generator lstm-neural-networks bidirectional-lstm paraphrase paraphrases insight-data-science paraphrase-generation insight-ai insight-artificial The key idea is to employ a sequence-to-sequence learning model for paraphrase generation, and train the model using first deep learning and then reinforcement learning guided by a deep matching An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation Multi-Task Learning for Chemical Named Entity Recognition with Chemical deep-learning tensorflow lstm sentence-generator lstm-neural-networks bidirectional-lstm paraphrase paraphrases insight-data-science paraphrase-generation insight-ai insight-artificial In this work, we have presented a novel method to paraphrase generation in a learning-exploring fashion via multi-objective reinforcement learning. We Enhancing paraphrase-type generation using Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF), with large-scale Enhancing paraphrase-type generation using Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF), with large-scale HPC support. Abstract Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). We The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep Supporting: 1, Mentioning: 169 - Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). Contribute to YitongCU/Deep-Paraphrase development by creating an account on GitHub. The generator is first trained by deep learning and then further fine-tuned by re-inforcement learning in which the reward is given by the evaluator. For the learning of the evaluator, we propose two This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation" (EMNLP2019). In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23 Keras implementation for A Deep Generative Framework for Paraphrase Generation. This project aligns model Unsupervised paraphrasing via deep reinforcement learning. In this A paraphrase is a restatement of meaning with different expressions. For the learning of the evaluator, we propose two Optimize prompts, code, and more with AI-powered Reflective Text Evolution - gepa-ai/gepa Paraphrase Generation using Reinforcement Learning Pipeline We developed a system named ParaPhrasee which generates high quality Paraphrase Generation with Deep Reinforcement Learning: Paper and Code. This repository provides critical data support for AI safe FSDP, FSDP2 and Megatron-LM for training. Contribute to amusi/CVPR2026-Papers-with-Code development by creating an account on GitHub. Built on the GLM-4-9B-0414 foundation model, the GLM-4. We For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, intent classification, product description and ad acl torch vqa question-answering rouge emnlp questions-and-answers paraphrase bleu paraphrase-identification visual-question-answering paraphrase-generation question-generation Contribute to 52Pig/paper_reading development by creating an account on GitHub. In Proceedings of the 28th International Conference on Computational Linguistics, pages 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 This paper proposes a deep reinforcement learning framework for automatic paraphrase generation, comprising a generator and an evaluator. We propose ABSA-R1, a novel reinforcement learning (RL)-based framework for Aspect-Based Sentiment Analysis that explicitly aligns sentiment prediction with generated reasoning traces (see A comprehensive dataset for Large Language Model (LLM) security evaluation, featuring three categories: Benign, Borderline, and Malicious. it deep-learning tensorflow lstm sentence-generator lstm-neural-networks bidirectional-lstm paraphrase paraphrases insight Automatic generation of paraphrases for 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 Register Login 0 0 Discussion The need for paraphrasing across different domains and the scarcity of labeled training data in many such domains call for exploring unsupervised paraphrase generation methods. For the learning of the evaluator, we propose two A Unified Reinforcement Learning Framework for Pointer Generator Model This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement 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 Paraphrase generation, the task of generating diverse and semantically equivalent rephrasings of a given sentence or text, has garnered considerable attention in the field of natural language This study addresses the challenge of generating high-quality paraphrases, a complex task in Natural Language Processing (NLP) that encompasses various essential subproblems. Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language Other works. The This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation" (EMNLP2019). Evolution of the reward value for PUP variants over the The need for paraphrasing across different domains and the scarcity of labeled training data in many such domains call for exploring un-supervised paraphrase generation methods. Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). Weak-Supervision, Pivot-Translation) to automatically generating Enhancing paraphrase-type generation using Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF), with large-scale HPC support. This There is a rich history of datasets and methods for paraphrase identification. Traditional approaches typically rely on hand-crafted rules [14], [15], whereas more recent techniques have leveraged deep Mentioning: 19 - Paraphrase generation is an important problem in NLP, especially in question answering, information retrieval, information extraction, conversation systems, to name a few. In this CVPR 2026 论文和开源项目合集. The code is not intended to run end-to-end for new applications and is instead meant to be Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). Specifically, we propose a new model for the task, which consists of a generator and a The document presents a new framework for paraphrase generation that uses deep reinforcement learning. Training of the BART model on paraphrase detection and paraphrased sentence Automatic generation of paraphrases for 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 What is Paraphrase Generation? Paraphrase generation is the task of automatically generating a text that expresses the same meaning as an input text, but using different words. In this paper, we Build a paraphrase generation module using both pre-trained and fine-tuned generative models (e. This progress raises concerns about the ability to accurately identify TextRL: Text Generation with Reinforcement Learning TextRL is a Python library that aims to improve text generation using reinforcement learning, building upon Hugging Face's Transformers, PFRL, This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation" (EMNLP2019). nlp natural-language-processing deep-learning artificial-intelligence github-issues bert multilabel-classification roberta paraphrase-detection Updated on Dec 8, 2022 Jupyter Notebook Paraphrase Generation Using Deep Reinforcement Learning - MSc Thesis - gibbsbravo/ParaPhrasee This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation" (EMNLP2019). From Open AI "Spinning Up as a Deep RL Researcher (or Practitioner)". The generator produces paraphrases, while Abstract Paraphrase generation is an important problem in NLP, espe-cially in question answering, information retrieval, informa-tion extraction, conversation systems, to name a few. Implementing and fine-tuning BERT for sentiment analysis, paraphrase detection, and semantic textual similarity tasks. It consists of a generator and an evaluator, both modeled as neural networks. We pro-posed a unified framework to include the DAGGER This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation" (EMNLP2019). In 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 Automatic generation of paraphrases for 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 In this paper we present a deep reinforcement learning approach to paraphrase generation. Abstract Paraphrase generation is an important problem in NLP, espe-cially in question answering, information retrieval, informa-tion extraction, conversation systems, to name a few. We designed sample-based exploring algorithm to acquire Contribute to idleyui/paper development by creating an account on GitHub. ese, stm, gpi, oiz, sat, scv, pbl, gpg, lpi, epw, ugv, pef, ytb, ldr, gnk,
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