Papers with Code 아카이브
Text Generation
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Text Generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task is more formally known as "natural language generation" in the literature.
Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Adversarial Ranking for Language Generation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Yale-LILY/SummerTime
공식 코드https://github.com/Yale-LILY/dart
공식 코드https://github.com/Yaoming95/Triple-to-Text
공식 코드https://github.com/YatinChaudhary/NCLM
공식 코드Explainable and Discourse Topic-aware Neural Language Understanding
https://github.com/YeLuoSuiYou/openstorypp
공식 코드Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling
https://github.com/YecanLee/2BeOETG
공식 코드Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework
https://github.com/YecanLee/Adaptive-Contrastive-Search
공식 코드Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation
https://github.com/YihongDong/CODEP
공식 코드CodePAD: Sequence-based Code Generation with Pushdown Automaton
https://github.com/Yinghao-Li/GuiGen
공식 코드Transformer-Based Neural Text Generation with Syntactic Guidance
https://github.com/Yixiao-Song/VeriScore
공식 코드VERISCORE: Evaluating the factuality of verifiable claims in long-form text generation
https://github.com/YouzhiTian/Structured-Content-Preservation-for-Unsupervised-Text-Style-Transfer
공식 코드Structured Content Preservation for Unsupervised Text Style Transfer
https://github.com/Yu-Qi-hang/ThinkRec
공식 코드https://github.com/YubaoZhao/ECG-Chat
공식 코드ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis
https://github.com/Yutong-gannis/TiSpell
공식 코드https://github.com/ZJULearning/AMI
공식 코드https://github.com/ZaidAli60/saylaniassignment3
공식 코드Exploring Transformers in Natural Language Generation: GPT, BERT, and XLNet
https://github.com/ZhaoqLiu/BreakNLI
공식 코드Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models
https://github.com/Zhenhang-Li/GlyphOnly
공식 코드First Creating Backgrounds Then Rendering Texts: A New Paradigm for Visual Text Blending
https://github.com/ZhihongShao/Planning-based-Hierarchical-Variational-Model
공식 코드Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
https://github.com/ZhouYuxuanYX/Benchmarking-and-Guiding-Adaptive-Sampling-Decoding-for-LLMs
공식 코드https://github.com/ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning
공식 코드https://github.com/aayush-shah14/peptidegpt
공식 코드https://github.com/abaheti95/QADialogSystem
공식 코드Fluent Response Generation for Conversational Question Answering
https://github.com/abaheti95/lol-rl
공식 코드Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models
https://github.com/abaheti95/toxichat
공식 코드Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts
https://github.com/abbey4799/hauser
공식 코드HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation
https://github.com/abdulhaim/lmrl-gym
공식 코드LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models
https://github.com/abhijitmishra/Thought2Text
공식 코드Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs)
https://github.com/abhinand5/tamil-llama
공식 코드https://github.com/abhinavkashyap/dct
공식 코드So Different Yet So Alike! Constrained Unsupervised Text Style Transfer
https://github.com/abisee/story-generation-eval
공식 코드Do Massively Pretrained Language Models Make Better Storytellers?
https://github.com/abrazinskas/sigir2022-opinion-summarization-tutorial
공식 코드Beyond Opinion Mining: Summarizing Opinions of Customer Reviews
https://github.com/acharkq/molca
공식 코드MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter
https://github.com/acharkq/prott3
공식 코드ProtT3: Protein-to-Text Generation for Text-based Protein Understanding
https://github.com/acmi-lab/mvp
공식 코드Model-tuning Via Prompts Makes NLP Models Adversarially Robust
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.