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/ratishsp/data2text-seq-plan-py
공식 코드Data-to-text Generation with Variational Sequential Planning
https://github.com/raunak-agarwal/factual-consistency-eval
공식 코드https://github.com/ravoxsg/efficient_unified_crs
공식 코드Parameter-Efficient Conversational Recommender System as a Language Processing Task
https://github.com/rayhuang880301/gptutor-extension
공식 코드GPTutor: a ChatGPT-powered programming tool for code explanation
https://github.com/raytsang123/clibe
공식 코드CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models
https://github.com/rcalef/hcb_infilling
공식 코드Towards Probabilistically-Sound Beam Search with Masked Language Models
https://github.com/rd20karim/M2T-Segmentation
공식 코드Motion2Language, unsupervised learning of synchronized semantic motion segmentation
https://github.com/rd20karim/m2t-interpretable
공식 코드https://github.com/rd20karim/synch-transformer
공식 코드Transformer with Controlled Attention for Synchronous Motion Captioning
https://github.com/reason-wang/nat
공식 코드https://github.com/recitalAI/QuestEval
공식 코드Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
https://github.com/reddy-lab-code-research/PPOCoder
공식 코드Execution-based Code Generation using Deep Reinforcement Learning
https://github.com/reidmcy/set-based-prompting
공식 코드https://github.com/researcher-tiger/moepg
공식 코드Reinforcement Learning Guided Multi-Objective Exam Paper Generation
https://github.com/researchmm/generate-it
공식 코드Unifying Multimodal Transformer for Bi-directional Image and Text Generation
https://github.com/richermans/AudioCaption
공식 코드https://github.com/ridgerchu/spikegpt
공식 코드SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
https://github.com/rikdz/GraphWriter
공식 코드Text Generation from Knowledge Graphs with Graph Transformers
https://github.com/rishidarkdevil/daam-i2i
공식 코드https://github.com/ritaramo/extra
공식 코드https://github.com/ritaranx/clingen
공식 코드https://github.com/rizar/actor-critic-public
공식 코드https://github.com/rkzheng99/villa
공식 코드ViLLa: Video Reasoning Segmentation with Large Language Model
https://github.com/rmusab/wrd-score
공식 코드WRDScore: New Metric for Evaluation of Natural Language Generation Models
https://github.com/roblox/cube
공식 코드https://github.com/roemmele/InSentive
공식 코드https://github.com/rohan-chaudhury/humane-speech-synthesis-through-zero-shot-emotion-and-disfluency-generation
공식 코드Humane Speech Synthesis through Zero-Shot Emotion and Disfluency Generation
https://github.com/rohitgandikota/erasing-llm
공식 코드https://github.com/roholazandie/EmpTransfo
공식 코드EmpTransfo: A Multi-head Transformer Architecture for Creating Empathetic Dialog Systems
https://github.com/roholazandie/topical_language_generation
공식 코드https://github.com/rosa-paper/rosa
공식 코드https://github.com/rosewang2008/language_modeling_via_stochastic_processes
공식 코드https://github.com/rossiyareich/inknhue
공식 코드inkn'hue: Enhancing Manga Colorization from Multiple Priors with Alignment Multi-Encoder VAE
https://github.com/rowanz/grover
공식 코드https://github.com/rowitzou/cg-nar
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.