Text Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 25
- 평가표
- 155
- 보존된 평가 기록
- 54
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/yinyueqin/denserewardrlhf-ppo
Preference-grounded Token-level Guidance for Language Model Fine-tuning
https://github.com/yiyc-kor/bert-study
https://github.com/ykrmm/ICLR_2020
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ykrmm/TREMBA
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ylXuu/ALiiCE
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
https://github.com/ylqi/count-anything
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/yoongtr/Grammar-Error-Correction-with-OpenNMT-tf
https://github.com/yoonkim/neural-qcfg
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer
https://github.com/youngerous/qtree
LongForm: Effective Instruction Tuning with Reverse Instructions
https://github.com/ypeleg/MinimalIsAllYouNeed
https://github.com/yuanfeisiyuetian/seqgan-modbusTCP
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
https://github.com/yuanmu97/secure-transformer-inference
https://github.com/yueyongjiao/Transformer-Contextual
https://github.com/yueyongjiao/attention_is_all_you_need_pytorch
https://github.com/yuh-zha/align
Text Alignment Is An Efficient Unified Model for Massive NLP Tasks
https://github.com/yuhangT/tf_bert
https://github.com/yuhui-zh15/nlg_metrics
https://github.com/yukikongju/R-Tutorial
https://github.com/yukw777/videoblip
https://github.com/yumoh/speech-keras
https://github.com/yurayli/image-caption-pytorch
https://github.com/yurayli/image_caption_pytorch
https://github.com/yurayli/stanford-cs224n-sol
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/yuuun/clip_pytorch
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/yuxiaw/openfactcheck
RARR: Researching and Revising What Language Models Say, Using Language Models
https://github.com/yuyay/chainer_nic
https://github.com/ywjawmw/shennong-tcm-evaluation-benchmark
https://github.com/yydai/bert_test
https://github.com/yyhappier/shortcutsuite
Reformulating Unsupervised Style Transfer as Paraphrase Generation
https://github.com/yzhao062/pyod
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
https://github.com/zankner/Transformer
https://github.com/zapplea/bert
https://github.com/zengyan-97/MultiT-C-Dialog
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue Generation
https://github.com/zer0sh0t/artificial_intelligence/tree/master/transformer
https://github.com/zfj1998/CodeBert-Code2Text
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.